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Quantitative vascular feature-based multimodality prediction model for multi-origin malignant cervical

Chunyan Li1, Rui Li2, Jinjing Ou1

  • 1Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.

Eclinicalmedicine
|March 3, 2025
PubMed
Summary

An artificial intelligence framework (DMFLNN) improved radiologist accuracy in diagnosing malignant cervical lymphadenopathy. This AI tool enhances diagnostic performance and may reduce unnecessary biopsies.

Keywords:
Artificial intelligenceCervical lymphadenopathyPrediction modelUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Cervical lymphadenopathy diagnosis faces challenges due to low inter-reader reproducibility in imaging interpretation.
  • A quantitative method is needed to improve the accuracy and consistency of diagnosing malignant cervical lymphadenopathy.

Purpose of the Study:

  • To develop and validate an AI framework integrating quantitative vascular features for assessing cervical lymphadenopathy.
  • To explore the utility of this AI framework in assisting radiologists.

Main Methods:

  • A dual-modality, multi-feature, fusion lymph node network (DMFLNN) was developed using ultrasound images from over 10,000 patients.
  • Quantitative vascular, morphological, and semantic features were fused to create the DMFLNN.
  • The AI's performance was compared against six radiologists on internal and external test cohorts.

Main Results:

  • DMFLNN achieved high AUCs (0.937 internal, 0.875 external).
  • Radiologist performance (AUC) improved significantly with DMFLNN assistance (senior: 0.814 to 0.836; junior: 0.778 to 0.847).
  • Inter-radiologist agreement and diagnostic accuracy increased, while false-positive rates decreased.

Conclusions:

  • The developed AI framework (DMFLNN) demonstrates potential to enhance diagnostic accuracy for cervical lymphadenopathy.
  • DMFLNN can improve radiologist performance and potentially reduce unnecessary biopsies.
  • Further clinical validation is recommended before widespread adoption.