Evidence map›Paper›PMID 37195443›Full record

ArticleAnalytical and bioanalytical chemistry2023

Rapid and precise detection of cancers via label-free SERS and deep learning.

Chang-Chun Xiong, Shan-Shan Zhu, Deng-Hui Yan, Yu-Dong Yao, Zhe Zhang, Guo-Jun Zhang, Shuo Chen

Open access · hybridAbstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
26.4field-weighted citation impact, top 1% 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

25 citing papers in PubMed, 74 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review: Detection of Cancer Biomarkers from a Clinical Perspective.International journal of molecular sciences · 2025
    Review
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  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

7 authors at 3 institutions in 1 country.

Chang-Chun XiongResearch Institute of Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China.
Shan-Shan ZhuResearch Institute of Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China. zhushanshan@nbu.edu.cn.
Deng-Hui YanResearch Institute of Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China.
Yu-Dong YaoResearch Institute of Medical and Biological Engineering, Ningbo University, Ningbo, 315211, China.
Zhe ZhangDepartment of Urology, First Affiliated Hospital, China Medical University, Shenyang, 110001, China.
Guo-Jun ZhangDepartment of Hematology, Shengjing Hospital, China Medical University, Shenyang, 110022, China.
Shuo ChenCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
Ningbo University · CNChina Medical University · CNNortheastern University · CN

Funding

Fundamental Research Funds for the Central Universities N2119002General scientific Research Project of Zhejiang Education Department Y202146723National Natural Science Foundation of China 61605025Natural Science Foundation of Zhejiang Province LQ23H180004Pro-gram for the Introduction of High-End Foreign Experts GXL20200218001
6 · The paper itself

Abstract

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker. Surface-enhanced Raman scattering (SERS), as a non-invasive ultrasensitive and label-free vibrational spectroscopy technique, can detect cancer-related biomedical changes in biofluids. In this paper, 110 serum samples were collected from 30 healthy controls and 80 cancer patients (including 30 bladder cancer (BC), 30 adrenal cancer (AC), and 20 acute myeloid leukemia (AML)). One microliter of blood serum was mixed with 1 μl silver colloid and then was air-dried for SERS measurements. After spectral data augmentation, one-dimensional convolutional neural network (1D-CNN) was proposed for precise and rapid identification of healthy and three different cancers with high accuracy of 98.27%. After gradient-weighted class activation mapping (Grad-CAM) based spectral interpretation, the contributions of SERS peaks corresponding to biochemical substances indicated the most potential biomarkers, i.e., L-tyrosine in bladder cancer; acetoacetate and riboflavin in adrenal cancer and phospholipids, amide-I, and α-Helix in acute myeloid leukemia, which might provide an insight into the mechanism of intelligent diagnosis of different cancers based on label-free serum SERS. The integration of label-free SERS and deep learning has great potential for the rapid, reliable, and non-invasive detection of cancers, which may significantly improve the precise diagnosis in clinical practice.

Indexed as

Adrenal Gland NeoplasmsDeep LearningUrinary Bladder NeoplasmsBiomarkers, TumorHumansProteomicsSpectrum Analysis, RamanBiomarkers, TumorCancer detectionDeep learningGradient-weighted class activation mapping (Grad-CAM)Surface-enhanced Raman scattering (SERS)

Identifiers

PMID37195443
PMCPMC10289912
OpenAlexW4376872844

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.