Evidence map›Paper›PMID 38195582›Full record

SynthesisRadiation oncology (London, England)2024

Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.

Tsair-Fwu Lee, Yang-Wei Hsieh, Pei-Ying Yang, Chi-Hung Tseng, Shen-Hao Lee, Jack Yang, Liyun Chang, Jia-Ming Wu, Chin-Dar Tseng, Pei-Ju Chao

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Radiation oncology (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Readiness for return to work and its influencing factors among head and neck cancer patients: a cross-sectional study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
    Article
  6. 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

10 authors.

Tsair-Fwu LeeMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Yang-Wei HsiehMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Pei-Ying YangMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Chi-Hung TsengMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Shen-Hao LeeMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Jack YangMedical Physics at Monmouth Medical Center, Barnabas Health Care at Long Branch, Long Branch, NJ, USA.
Liyun ChangDepartment of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 840, Taiwan, ROC.
Jia-Ming WuHeavy Ion Center of Wuwei Cancer Hospital, Gansu Wuwei Academy of Medical Sciences, Gansu Wuwei Tumor Hospital, Wuwei, Gansu Province, China.
Chin-Dar TsengMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
Pei-Ju ChaoMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC. pjchao99@gmail.com.

Funding

National Science and Technology Council 110-2221-E-992-005-MY2, 111-2221-E-992-016-MY2
6 · The paper itself

Abstract

purposeThe study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL AND

methodsThe study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate.

resultsThe study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters.

conclusionThe study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.

Indexed as

Head and Neck NeoplasmsXerostomiaHumansNatural Language ProcessingNeural Networks, ComputerProbabilityArtificial intelligenceConvolutional neural networksHead and neck cancerMeta-analysisNatural language processingNormal tissue complication probability predictionRadiation therapySquamous cell carcinoma of the head and neck

Identifiers

PMID38195582
PMCPMC10775485

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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.