Abstract
BackgroundBased on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM.MethodsSeventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity.ResultsNo clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists’ assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively.ConclusionT1CE–based radiomics showed better classification performance compared with radiologists’ assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.
Highlights
Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progres‐ sionin Glioblastoma multiforme (GBM) patients after standard treatment, which isa critical issue associated with survival
Maximal safe surgical resection followed by concurrent chemoradiotherapy (CCRT) with temozolomide (TMZ) and adjuvant TMZ has been a standard treatment, the prognosis of GBM patients is still very poor
According to the Response Assessment in Neuro-Oncology (RANO) criteria [3], the current strategy to distinguish pseudoprogression from true progression heavily depends on continuous follow-up MRI examinations
Summary
Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progres‐ sionin GBM patients after standard treatment, which isa critical issue associated with survival. The pseudoprogression is a treatment-related change within 12 weeks after the completion of CCRT, including inflammation, radiation effects, ischemia and increased vascular permeabilityand contrast enhancement on MR imaging [3]. It is difficult to differentiate them with conventional MRI sequences because pseudoprogression can mimic true progression in terms of tumor location, morphology, and enhancement patterns [4]. Their treatments and prognosis are completely different [5]. It is crucial to develop an effective method to differentiate pseudoprogression from true progression as early as possible
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