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AI-FLEET: Phase I-Multimodal Deep Learning Model for Phyllodes Tumor Classification.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) models integrating ultrasound and clinical data accurately differentiate benign from malignant fibroepithelial breast lesions. This AI-assisted approach improves diagnostic accuracy for phyllodes tumors (PTs), reducing misclassification risks.

Keywords:
Artificial intelligenceBreast ultrasoundDeep learningFibroepithelial lesionPhyllodes tumor

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Fibroepithelial breast lesions, including fibroadenomas and phyllodes tumors (PTs), pose diagnostic challenges on needle biopsy.
  • Misclassification can lead to unnecessary surgeries for benign lesions or delayed treatment for malignant PTs.
  • The AI-FLEET program aims to enhance diagnostic accuracy by integrating various data types.

Purpose of the Study:

  • To develop and evaluate AI models for distinguishing benign from borderline/malignant PTs using ultrasound and clinical data.
  • To assess the performance of different deep learning architectures in classifying fibroepithelial lesions.

Main Methods:

  • Retrospective analysis of 81 patients with histologically confirmed PTs (65 benign, 16 borderline/malignant).
  • Training multimodal deep learning models (ConvNeXt, ResNet18) using ultrasound images and clinical variables (age, BMI, race, menopausal status, echogenicity, tumor size).
  • Evaluation using subject-stratified five-fold cross-validation.

Main Results:

  • Multimodal ConvNeXt and ResNet18 models achieved high accuracy (0.91-0.92) and AUC (0.94).
  • Ultrasound-only and clinical-only models showed lower performance (AUCs 0.89 and 0.78, respectively).
  • Intratumoral heterogeneity was identified as a key predictive feature via saliency analysis.

Conclusions:

  • Multimodal deep learning models effectively differentiate benign from borderline/malignant PTs.
  • AI-assisted assessment of fibroepithelial lesions is feasible, showing high diagnostic accuracy.
  • Future work (Phase II) will incorporate histopathology and benign fibroadenoma cases for enhanced integration.