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Mouse Models of Cancer Study02:43

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Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter

Huiling Liu1,2, Mi Lao3, Yalin Zhang1

  • 1Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China.

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|October 2, 2024
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Fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics machine learning (ML) can effectively differentiate cervical cancer subtypes. This noninvasive approach shows promise for diagnosing and managing locally advanced cervical cancer.

Keywords:
ACPETSCCadenocarcinomalocally advanced cervical cancerpositron emission tomographyradiomicssquamous cell carcinoma

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging
  • Machine Learning

Background:

  • Cervical cancer diagnosis relies on invasive methods.
  • Distinguishing between cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC) is crucial for treatment planning.
  • Novel noninvasive diagnostic tools are needed for improved patient management.

Purpose of the Study:

  • To evaluate the diagnostic performance of 18F-FDG PET/CT radiomics-based ML models.
  • To classify cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC).
  • To assess the potential of radiomics as a noninvasive diagnostic approach.

Main Methods:

  • Retrospective collection of pretreatment 18F-FDG PET/CT data from 227 patients with locally advanced cervical cancer.
  • Extraction and selection of radiomics features using Pearson correlation and least absolute shrinkage and selection operator regression.
  • Application of six ML algorithms, with the lightGBM algorithm selected for the best-performing model.
  • Validation of model performance using accuracy, sensitivity, specificity, and AUC, with DeLong test for comparisons.

Main Results:

  • The lightGBM-based PET radiomics model achieved high accuracy (0.915) and AUC (0.851) in the internal validation cohort.
  • PET radiomics model significantly outperformed the CT radiomics model (accuracy: 0.661; AUC: 0.513).
  • The PET radiomics model demonstrated good discrimination in the external validation cohort (AUC = 0.730).

Conclusions:

  • The lightGBM-based PET radiomics model shows significant potential for predicting histological subtypes of locally advanced cervical cancer.
  • This approach offers a promising noninvasive method for the diagnosis and management of cervical cancer.
  • Further research can explore integrating radiomics into clinical decision-making for cervical cancer.