Crompton, David J.

Abstract Background The current treatment of glioblastoma involves resection followed by radiotherapy (RT) and temozolomide. Rapid early progression (REP) of tumor in the surgery-to-radiotherapy interval (SRI) has been described using categorical or semi-quantitative assessments and is associated with inferior clinical outcomes. This study seeks to establish and validate a quantitative definition for REP and examine how REP could inform future therapy. Methods Volumes of tumor and resection cavity at 3 time points—preoperative, postoperative and RT-planning (pre-RT)—were contoured among patients who had undergone standard of care. A minimal, but clinically appreciable, threshold for defining REP based on change in tumor volume was established, validated, and subsequently used to assess actionable predictors of REP and its prognostic value. Results An increase of >2 cc in volume or a new satellite tumor between the post-operative to planning MRI was found to be a sensitive, pragmatic, and statistically significant cutoff for REP (P = .002). Actionable predictors such as SRI of 6 weeks and a > 5 cc residual immediate post-operative tumor volume were found to be statistically significant predictors of REP. REP was also found to be a statistically significant prognostic indicator for overall survival (P = .002) and progression-free survival. Conclusion These findings support that an increase in tumor volume between post-operative and pre-RT MRI of >2 cc is the ideal working definition of REP in this clinical setting. Furthermore, REP could serve as a prognostic ­biomarker to track treatment response. Additionally, 2 actionable predictors of REP, post-op residual tumor volume and SRI, are identified.

Abstract Introduction/ Background Holmium laser enucleation of the prostate (HoLEP) is an increasingly popular size-independent technique of treating male voiding dysfunction due to benign prostatic hypertrophy. Some patients after HoLEP may develop clinically significant prostate cancer and opt for definitive treatment with external beam radiation therapy (EBRT). Little is known about the safety of EBRT after HoLEP and how it may functionally impact voiding after HoLEP has altered the anatomy of the prostate. Our study aimed to assess patient-reported voiding outcomes following EBRT after HoLEP with a focus on incontinence related patient outcomes. Methods/ Materials This study was conducted with approval from our hospital’s institutional review board. Patients that underwent HoLEP followed by EBRT were identified and data was collected in a retrospective nature from a single surgeon HoLEP cohort over the past 4 years (2019–2023). Patient demographics, disease and radiation therapy characteristics, radiation therapy, and baseline voiding symptoms were recorded. Current functional voiding outcomes were also collected via phone-call or portal communication in a cross-sectional manner with questions pertaining to type of incontinence, IPSS quality of life score, and administration of the Michigan Incontinence Symptom Index (M-ISI). Adverse events encountered during follow-up were recorded. Results 24 patients were identified who received RT for prostate cancer after HoLEP with an average age of 73.6 (+/- 5.3). One third of patients reported no incontinence whatsoever after radiation and of those who experienced incontinence, the majority felt that it was not worsened after radiation. Median IPSS QoL score following radiation was 1 (range 0–6), median M-ISI Severity Score was 4 out of a maximum of 32, and median M-ISI Bother Score was 0 out of a maximum of 8. One patient developed a bladder neck contracture (BNC) approximately 1 year following his radiation therapy (approximately 18 months after HoLEP) causing bothersome incontinence and LUTS. Conclusions In our cohort most patients who received RT after HoLEP reported a high urinary-symptom related quality of life and a low rate of urinary incontinence. One patient who received SBRT suffered a BNC which is a known adverse event with RT but given our small sample size it remains unclear if the risk is higher in patients receiving RT after HoLEP. Larger studies should focus on examining the rate of bladder neck contracture in patients receiving RT after HoLEP, particularly focusing on whether the degree of dose fractionation may impact their development.

Abstract INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD We included newly diagnosed GBM patients who met the inclusion criteria, including a new or increasing enhancing lesion size within the original radiation field and who had clinical, medication, and any histopathology available. The interpretation of the MR images at the image inclusion time point had to be indeterminate. We labeled those who subsequently were stable or improved on imaging and clinically as PSP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PSP or TP based on the histological diagnosis. We performed skull stripping, coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model. We used several augmentation techniques and five-fold cross-validation to achieve more robust predictions. RESULT We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (ranged 70%-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560(ranged 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05%(SD 9.11) mean specificity. CONCLUSION We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

Abstract PURPOSE Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care. METHODS The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index). RESULTS From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age > 65 years was predictive of decreased OS (p < 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71. CONCLUSIONS We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.