Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations

Mod Pathol. 2021 Nov;34(11):2028-2035. doi: 10.1038/s41379-021-00850-6. Epub 2021 Jun 10.


Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion ('referral' test set), and on an externally stained set from outside institutions ('externally stained' test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Area Under Curve
  • Cell Proliferation
  • Deep Learning / classification*
  • Diagnosis, Differential
  • Humans
  • Image Processing, Computer-Assisted
  • Mesothelioma / classification
  • Mesothelioma / diagnosis*
  • Mesothelioma, Malignant / classification
  • Mesothelioma, Malignant / diagnosis*
  • Neural Networks, Computer
  • Pleural Neoplasms / classification
  • Pleural Neoplasms / diagnosis*
  • Prognosis
  • ROC Curve
  • Sensitivity and Specificity