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Comparison of iRECIST and RECIST 1.1 for Evaluating Immunotherapy in Melanoma and Non-Small Cell Lung Cancer

In a retrospective study conducted at the University Hospital Cologne, the radiological criteria iRECIST and RECIST 1.1 were compared for assessing…

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University Hospital Cologne: Comparison of iRECIST and RECIST 1.1 for Evaluating Immunotherapy in Melanoma and Non-Small Cell Lung Cancer

A retrospective study conducted at University Hospital Cologne compared two criteria for assessing therapeutic response to immunotherapy: iRECIST and…

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Woman reading the article on multiparametric MRI in prostate cancer treatments on her laptop

Use of Multiparametric MRI in Prostate Cancer Treatments: A Prospective Study

The prospective study conducted by LMU Klinikum München investigates the effectiveness of vascular-targeted photodynamic therapy (VTP) and…

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LMU Klinikum Munich: Monitoring Prostate Cancer Treatments with VTP and HIFU - The Use of Multiparametric MRI

The prospective study conducted by LMU Klinikum Munich investigates the treatment of localized prostate cancer using two techniques: vascular-targeted…

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Virtual examination of human lungs on a modern interface screen

Early Detection of Treatment Response in Lung Cancer Using Delta-Radiomics Features

The study conducted by Heidelberg University Hospital investigates the use of diffusion-weighted MRI (DWI) to predict early treatment outcomes in…

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The image serves as a graphical abstract, displaying visuals from the study, ranging from MRI scans to the analysis of delta-radiomics texture features

Heidelberg University Hospital: Delta-Radiomics Features from ADC Maps as Early Predictors of Treatment Success in Lung Cancer Therapy

In this prospective study conducted by Heidelberg University Hospital, researchers investigated whether changes in radiomic features from…

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Doctors looking at MRI scans to evaluate a glioblastoma.

Optimizing Glioblastoma Imaging: Enhancing MRI Efficiency and Quality with Deep Learning

This study investigates the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients, aiming to reduce scan time and improve…

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Three important sequences (FLAIR, T2, T1 with contrast agent) in the assessment of glioblastoma

University Hospital Tübingen: Advancing MRI Efficiency in Glioblastoma Care with Deep Learning

This study explores the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients. Glioblastomas, known for being the most…

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Illustrations of solid pseudopapillary neoplasms and pancreatic neuroendocrine neoplasms

Heidelberg University Hospital: Key Imaging Features to Distinguish Solid Pseudopapillary Neoplasms from Pancreatic Neuroendocrine Neoplasms

Solid pseudopapillary neoplasms (SPNs), or Frantz tumors, are rare pancreatic tumors accounting for 2-3% of all pancreatic neoplasms. These tumors…

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