0000000000749703

AUTHOR

A. Kreft

showing 1 related works from this author

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

2021

Background Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS. Patients and methods Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS. The Cancer Genome Atlas cohort (240 patients) served as training and validation set. A second, multicenter cohort (51 patients) served as an additional test set. The use of the DL model (DLM) as a clinical decision support system was evaluated by nine pathologists with different levels of expertise. For prognosis prediction, 139 slides from 85 patients with leiomyosarcom…

0301 basic medicineLeiomyosarcomamedicine.medical_specialtySoft Tissue Neoplasms03 medical and health sciences0302 clinical medicineDeep LearningmedicineHumansRetrospective StudiesReceiver operating characteristicProportional hazards modelbusiness.industrySoft tissue sarcomaHazard ratioDigital pathologySarcomaHematologymedicine.diseasePrognosisConfidence interval030104 developmental biologyOncology030220 oncology & carcinogenesisCohortRadiologybusinessAnnals of oncology : official journal of the European Society for Medical Oncology
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