Yang, Michael

Abstract Background:The standard of care for elderly glioblastoma (GBM) patients is 40 Gy in 15 fraction radiotherapy with temozolomide (TMZ). However, this regimen has a lower biologic equivalent dose (BED) compared to the Stupp regimen of 60 Gy in 30 fractions. We hypothesize that accelerated hypofractionated radiation of 52.5 Gy in 15 fractions (BED equivalent to Stupp) will have superior survival compared to 40 Gy in 15 fractions.Methods:Elderly patients (≥ 65 years old) who received hypofractionated radiation with TMZ from 2010-2020 were included in this analysis. Overall survival (OS) and progression free survival (PFS) were defined as the time elapsed between surgery/biopsy and death from any cause or progression. Baseline characteristics were compared between patients who received 40 Gy and 52.5 Gy. Univariable and multivariable analyses were performed.Results: Sixty-six newly diagnosed patients were eligible for analysis. Thirty-nine patients were treated with 40 Gy in 15 fractions while twenty-seven were treated with 52.5 Gy in 15 fractions. Patients had no significant differences in age, sex, methylation status, or performance status. Overall survival (OS) was superior in the 52.5 Gy group (14.1 months) when compared to the 40 Gy group (7.9 months, p = 0.011). Isoeffective dosing to 52.5 Gy was shown to be an independent prognostic factor for improved OS on multivariable analysis. Conclusions:Isoeffective dosing to 52.5 Gy in 15 fractions was associated with superior OS compared to standard of care 40 Gy in 15 fractions. These hypothesis generating data support accelerated hypofractionation in future prospective trials.

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.