Evidence map›Paper›PMID 35521553›Full record

ArticleComputational and structural biotechnology journal2022

Machine learning application in personalised lung cancer recurrence and survivability prediction.

Yang Yang, Li Xu, Liangdong Sun, Peng Zhang, Suzanne S Farid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 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

35 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024
    Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

5 authors.

Yang YangDepartment of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK.
Li XuDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China.
Liangdong SunDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China.
Peng ZhangDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China.
Suzanne S FaridDepartment of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

Indexed as

ANNs, artificial neural networksANOVA, analysis of varianceAUC, the area under the ROC curveCART, classification and regression treeCNV, copy number variationDecision treeDTs, decision treesFFNN, Feedforward neural networksLS-SVM, least-squares support vector machineLUAD, lung adenocarcinomaLung cancerLUSC, lung squamous cell carcinomaMachine learningML, machine learningNSCLC, non-small cell lung cancerPersonalized diagnosis and prognosisROC, receiver operating characteristicSVMs, support vector machinesTCGA, The Cancer Genome AtlasTNM, a common cancer staging system while T, N and M refers to tumour, node and metastasis

Identifiers

PMID35521553
PMCPMC9043969

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.