首页|Recent Findings in Machine Learning Described by a Researcher from Erasmus Unive rsity Medical Center Cancer Institute (Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics)
Recent Findings in Machine Learning Described by a Researcher from Erasmus Unive rsity Medical Center Cancer Institute (Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Rotterdam, Netherlands, by NewsRx journalists, research stated, "Malignant peripheral nerve sheath tumor s (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Curre nt magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasi ve biopsies." The news journalists obtained a quote from the research from Erasmus University Medical Center Cancer Institute: "This study aims to develop a radiomics model u sing quantitative imaging features and machine learning to distinguish MPNSTs fr om BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi -automatically segmented on MRI scans, and radiomics features were extracted usi ng the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100 x random-s plit cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1- weighted (T1w) MRI radiomics model outperformed others with an area under the cu rve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance per formance."
Erasmus University Medical Center Cancer InstituteRotterdamNetherlandsEuropeCyborgsEmerging TechnologiesMach ine LearningRisk and Prevention
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