Evidence map›Paper›PMID 40241186›Full record

ArticleJournal of nanobiotechnology2025

AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

Haiting Cao, Xiaofeng Wu, Huayi Shi, Binbin Chu, Yao He, Houyu Wang, Fenglin Dong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Haiting Cao *Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China.
Xiaofeng Wu *Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, 215031, Jiangsu, China.
Huayi Shi *Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China.
Binbin ChuSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China. bbchu@suda.edu.cn.
Yao HeSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China. yaohe@suda.edu.cn.
Houyu WangSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China. houyuwang@suda.edu.cn.
Fenglin DongDepartment of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, 215031, Jiangsu, China. fldong@suda.edu.cn.

Funding

National Natural Science Foundation of China 22204117National Natural Science Foundation of China T2321005Special Project for Clinical Key Diseases in Diagnosis and Treatment Technology of Suzhou LCZX202104
6 · The paper itself

Abstract

backgroundLymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7-15 days) and large-field view (e.g., > 1000 × 1000 μm

resultsHerein, we develop a molecular imaging strategy for minimally invasive lymphoma diagnosis. By spreading lymphoma tissue sections tightly on a surface-enhanced Raman scattering (SERS) chip, label-free images of DNA double strand breaks (DSBs) in 30 × 30 μm

conclusionsThe molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma.

Indexed as

Artificial IntelligenceLymphomaMolecular ImagingSpectrum Analysis, RamanDeep LearningHumansLymphoma, Non-HodgkinNeural Networks, ComputerConvolutional neural networkLymphomaMinimally invasive diagnosisSERS

Identifiers

PMID40241186
PMCPMC12001690

What Socratic 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 Socratic graph.