0000000001169189

AUTHOR

Ilaria Merli

showing 2 related works from this author

Middle patellar tendon to posterior cruciate ligament (PT–PCL) and normalized PT–PCL: New magnetic resonance indices for tibial tubercle position in …

2018

BACKGROUND To demonstrate whether the distance between the middle point of the patellar tendon and posterior cruciate ligament (PT-PCL) calculated on a single axial MR image could be an alternative measure to tibial tubercle-PCL (TT-PCL) distance for TT lateralization without the need of imaging processing. To show that normalization of PT-PCL (nPT-PCL) against the maximum diameter of the tibial plateau may help to identify patients with patellar instability (PI). METHODS MR scans of 30 patients (13 females, age 32 ± 13 years) with known PI and 60 patients (31 females, age 39 ± 19 years) with no history of PI were reviewed. Two operators calculated TT-PCL, and PT-PCL nPT-PCL. Intraclass cor…

AdultJoint InstabilityMaleAdolescentIntraclass correlationInterobserver reproducibility030218 nuclear medicine & medical imagingPatellofemoral Joint03 medical and health sciences0302 clinical medicineMcNemar's testPatellar LigamentImage Processing Computer-AssistedmedicineHumansOrthopedics and Sports MedicineIn patient030222 orthopedicsTibiamedicine.diagnostic_testReceiver operating characteristicbusiness.industryReproducibility of ResultsMagnetic resonance imagingMiddle AgedMagnetic Resonance ImagingPatellar tendonmedicine.anatomical_structureROC CurvePosterior cruciate ligamentFemalePosterior Cruciate LigamentbusinessNuclear medicineThe Knee
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Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study

2021

Purpose: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software. Methods: Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ± 16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were la…

AdultMaleSpine.ScannerAdolescentVertebral lesionBone NeoplasmsFeature selectionMachine learningcomputer.software_genre030218 nuclear medicine & medical imagingMachine LearningYoung Adult03 medical and health sciences0302 clinical medicineSoftwareRadiomicsArtificial IntelligenceHumansMedicineRadiology Nuclear Medicine and imagingChildAgedRetrospective StudiesAged 80 and overTraining setbusiness.industryMean ageGeneral MedicineMiddle AgedMagnetic Resonance Imaging030220 oncology & carcinogenesisNeoplasmFemaleArtificial intelligenceRadiomicDifferential diagnosisbusinesscomputerSoftware
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