Priya, Sarv

Introduction Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging–derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). Methodology A retrospective, institutional review board–approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. Results For all patients combined, the clinical feature–derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. Conclusions When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature–derived model for predicting PFS.

AbstractObjectiveCT pulmonary angiography (CTPA) is one of the most commonly ordered CT imaging tests. It is often believed to be overutilised with few recent studies showing a yield of less than 2%. This study aimed to determine the overall positivity rate of CTPA examinations and understand the factors that affect the yield of the CTPA examination.MethodsWe retrospectively analysed 2713 patients who received the CTPA exam between 2016 and 2018. Type of study ordered (CTPA chest or CTPA chest with abdomen and pelvis CT), patient location (emergency department (ED), outpatient, inpatient, intensive care unit (ICU)) and patient characteristics—age, sex and body mass index (BMI) were recorded. A logistic regression analysis was performed to determine what factors affect the positivity rate of CT scans for pulmonary embolism (PE).ResultsWith 296 positive test results, the overall CTPA positivity was 10.9%. Male sex was associated with higher CTPA positivity, gender difference was maximum in 18-year to 35-year age group. Overweight and obese patients had significantly higher positivity as compared with BMI&lt;25 (p&lt;0.05). Higher positivity rate was seen in the BMI 25–40 group (11.9%) as compared with BMI&gt;40 (10.1%) (p&lt;0.05). Significant difference (p&lt;0.001) was also found in CTPA examination yield from ICU (15.3%) versus inpatients (other than ICU) (12.4%) versus ED (9.6%), and outpatients (8.5%). The difference in CTPA yield based on the type of CT order (CTPA chest vs CTPA chest with CT abdomen and pelvis), patient’s age and sex was not significant.ConclusionCTPA yield of 10.9% in this study is comparable to acceptable positivity rate for the USA and is higher than recent studies showing positivity of &lt;2%. Patient characteristics like obesity and ICU or inpatient location are associated with higher rate of CT positivity.