Radiomic analysis to predict histopathologically confirmed pseudoprogression in glioblastoma patients

  title={Radiomic analysis to predict histopathologically confirmed pseudoprogression in glioblastoma patients},
  author={Anna Sophia McKenney and Emily Weg and Tejus A. Bale and Aaron Wild and Hyemin Um and Michael J. Fox and Andrew L. Lin and Jonathan T. Yang and P Yao and Maxwell Birger and Florent Tixier and Matthew Sellitti and Nelson S. Moss and Robert J. Young and Harini Veeraraghavan},
  journal={Advances in Radiation Oncology},


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The Biological Meaning of Radiomic Features.
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Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning
In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.
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Machine Learning Model to Predict Pseudoprogression Versus Progression in Glioblastoma Using MRI: A Multi-Institutional Study (KROG 18-07)
This work investigated the feasibility of machine learning algorithm to distinguish pseudoprogression from progressive disease in glioblastoma patients, and developed a deep learning model based on the whole dataset.
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.
A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma
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This work pairs a succinct description of the histological, biological, and molecular characteristics of recurrent glioma with recommendations for how to better standardize and implement quality pathological assessment into patient management.
Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
This MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question.