Evidence map›Paper›PMID 40237056›Full record

ArticleCurrent radiopharmaceuticals2025

Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma.

Yibin Liu, Xianwen Wang, Jiongyi Li, Junxiao Gao, Bin He, Xianlong Wang, Lianfang Tian, Bin Li, Qianhui Qiu

Abstract read
PubMed Publisher
In one paragraph

Article in Current radiopharmaceuticals, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Yibin LiuDepartment of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Xianwen WangDepartment of Otolaryngology & Head and Neck Surgery, Ganzhou People's Hospital, Ganzhou, 341000, China.
Jiongyi LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
Junxiao GaoDepartment of Otolaryngology & Head and Neck Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Bin HeDepartment of Otolaryngology & Head and Neck Surgery, Wuzhou Red Cross Hospital, Wuzhou, 543002, China.
Xianlong WangDepartment of Imaging Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Lianfang TianSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
Bin LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
Qianhui QiuDepartment of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.

Funding

Guangdong Provincial People's Hospital Senior Talent Introduction and Research Initiation Fund KY012021204
6 · The paper itself

Abstract

introductionNasopharyngeal Carcinoma (NPC) exhibits high incidence in southern China. Despite improved survival with intensity-modulated radiotherapy (IMRT), 10%-20% of patients experience local recurrence. Traditional TNM staging fails to reflect tumor heterogeneity, necessitating robust recurrence prediction models. This study aimed to develop an MRIbased NPC recurrence prediction model by integrating radiomics, deep learning, and clinical features.

methodsA total of 184 pathologically confirmed NPC patients receiving radical radiotherapy were included. After propensity score matching (1:1), 136 cases were analyzed. Stacked denoising autoencoder (SDAE) extracted deep features from contrast-enhanced T1-weighted MRI. Radiomic features (morphology, texture, first-order statistics), clinical parameters (gender, age, TNM stage), and SDAE features were combined to construct 12 models using SVM, MLP, logistic regression (LR), and random forest (RF). Performance was evaluated via AUC, accuracy, sensitivity, and specificity, with external validation (91 cases).

resultsModel 1 (radiomics + SDAE + clinical features + SVM) achieved the highest AUC (0.89, 95% CI: 0.84-0.93), accuracy (81.5%), sensitivity (67.3%), and specificity (97.9%). External validation showed AUC 0.83, sensitivity 88.9%, and specificity 78%. The DeLong test confirmed no significant AUC difference between internal and external cohorts (P >0.05). DISCUSSION: The fusion of SDAE-enhanced features outperformed traditional radiomics. SVM demonstrated optimal performance in small samples, likely due to its high-dimensional feature handling and anti-overfitting capability. Limitations include single-center retrospective design and lack of functional imaging (DWI/PET) or molecular markers (EBV-DNA). Future multicenter prospective studies and multimodal data integration are warranted to enhance biological interpretability and clinical utility.

conclusionThis model provides a tool for early recurrence risk stratification and personalized therapy optimization, advancing precision medicine in NPC management.

Indexed as

Magnetic Resonance ImagingNasopharyngeal CarcinomaNasopharyngeal NeoplasmsNeoplasm Recurrence, LocalAdultAgedAutoencoderDeep LearningFemaleHumansMaleMiddle AgedRadiotherapy, Intensity-ModulatedRetrospective Studiesmagnetic resonance imaging.Nasopharyngeal carcinomaprediction modelrecurrencestacked denoising autoencodersupport vector machine

Identifiers

PMID40237056

What OpenQuestion holds

Textmetadata
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.