Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using mr imaging

Fernando Navarro, Hendrik Dapper, Rebecca Asadpour, Carolin Knebel, Matthew B. Spraker, Vincent Schwarze, Stephanie K. Schaub, Nina A. Mayr, Katja Specht, Henry C. Woodruff, Philippe Lambin, Alexandra S. Gersing, Matthew J. Nyflot, Bjoern H. Menze, Stephanie E. Combs, Jan C. Peeken

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Original languageEnglish
Article number2866
Issue number12
StatePublished - Jun 2 2021


  • Artificial intelligence
  • Convolutional neural networks
  • Deep learning
  • MRI
  • Machine learning
  • Soft-tissue sarcomas
  • Tumor grading


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