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An Interpretable Radiomics Model Integrating Ultrasound and Clinical Features for Multiclass Classification of

Yin Zheng1, Chen Chen2, Hongwei You3

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Summary
This summary is machine-generated.

A new AI model, ALNIP, accurately classifies axillary lymph node (ALN) lesions using ultrasound. This tool aids in noninvasive diagnosis, improving tumor staging and treatment decisions for better patient outcomes.

Keywords:
Axillary lymph nodeInterpretablePredictionRadiomicsUltrasound

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate axillary lymph node (ALN) classification is crucial for cancer staging and treatment.
  • Current ultrasound interpretation by radiologists has limitations in differentiating benign, metastatic carcinoma, and lymphoma.
  • Improved diagnostic tools are needed for precise preoperative ALN assessment.

Purpose of the Study:

  • To develop and validate the Axillary Lymph Node Interpretable Prediction (ALNIP) model.
  • To classify ALNs into benign, metastatic carcinoma, or lymphoma using ultrasound-based radiomics and clinical data.
  • To compare the ALNIP model's performance against radiologists' assessments.

Main Methods:

  • A multicenter retrospective study involving 1480 ALNs.
  • Extraction and selection of radiomics features from ultrasound images using ElasticNet regression.
  • Development of the ALNIP model integrating radiomics and clinical variables, validated across multiple datasets.

Main Results:

  • The Logistic-Radiomics model achieved a MicroAUC of 0.835.
  • The ALNIP model demonstrated high performance with MicroAUCs of 0.924 (test set), 0.905 (external validation 1), and 0.853 (external validation 2).
  • The ALNIP model significantly outperformed radiologists of all experience levels.

Conclusions:

  • The ALNIP model provides a reliable and interpretable method for preoperative ALN status prediction.
  • It shows significant potential for noninvasive diagnosis, especially for challenging ALNs on ultrasound.
  • The model can aid in clinical decision-making, impacting tumor staging and treatment strategies.