Computer-aided segmentation and machine learning of integrated clinical and diffusion-weighted imaging parameters for predicting lymph node metastasis in endometrial cancer

Lan Yan Yang, Tiing Yee Siow, Yu Chun Lin, Ren Chin Wu, Hsin Ying Lu, Hsin Ju Chiang, Chih Yi Ho, Yu Ting Huang, Yen Ling Huang, Yu Bin Pan, Angel Chao, Chyong Huey Lai, Gigin Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

21 Scopus citations

Abstract

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortal-ity. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging be-fore surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10−3 mm2/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10−2), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance sur-passing the conventional ADC and size criteria.

Original languageEnglish
Article number1406
Pages (from-to)1-15
Number of pages15
JournalCancers
Volume13
Issue number6
DOIs
StatePublished - 02 03 2021

Bibliographical note

Publisher Copyright:
© by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Computer-aided
  • Diffusion-weighted imaging
  • Endometrial cancer
  • Lymph node
  • Machine learning
  • Magnetic resonance
  • Metas-tasis
  • Radiomics

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