Abstract Background Glioblastoma is the most common brain tumor in adults. The mechanisms leading to glioblastoma are not well understood but animal studies support that inactivation of tumor suppressor genes in neural stem cells (NSC) is required and sufficient to induce glial cancers. This suggests that the NSC niches in the brain may harbor cancer stem cells (CSCs), Thus providing novel therapy targets. We hypothesize that higher radiation doses to these NSC niches improve patient survival by eradicating CSCs. Methods 55 adult patients with Grade 3 or Grade 4 glial cancer treated with radiotherapy at UCLA between February of 2003 and May of 2009 were included in this retrospective study. Using radiation planning software and patient radiological records, the SVZ and SGL were reconstructed for each of these patients and dosimetry data for these structures was calculated. Results Using Kaplan-Meier analysis we show that patients whose bilateral subventricular zone (SVZ) received greater than the median SVZ dose (= 43 Gy) had a significant improvement in progression-free survival if compared to patients who received less than the median dose (15.0 vs 7.2 months PFS; P = 0.028). Furthermore, a mean dose >43 Gy to the bilateral SVZ yielded a hazard ratio of 0.73 (P = 0.019). Importantly, similarly analyzing total prescription dose failed to illustrate a statistically significant impact. Conclusions Our study leads us to hypothesize that in glioma targeted radiotherapy of the stem cell niches in the adult brain could yield significant benefits over radiotherapy of the primary tumor mass alone and that damage caused by smaller fractions of radiation maybe less efficiently detected by the DNA repair mechanisms in CSCs.

Purpose

We compared the dosimetry of brachyablation (BA) and stereotactic ablative radiotherapy (SABR) in the treatment of liver metastases.

Methods and materials

Treatment plans for 10 consecutive liver metastasis patients, treated with SABR, were replanned for BA. BA treatment was planned using five 12 Gy fractions to the same planning target volume (PTV) used for SABR. Dosimetric parameters were compared using a Student's paired t test.

Results and conclusions

BA and SABR plans had similar mean volume receiving 100% of the prescribed dose (94.1% vs. 93.9% of PTV, p = 0.8). Mean volume receiving 150% of the prescribed dose for BA was 63.6%, whereas for SABR it was 0. The minimum dose to the PTV was 65.8% for BA, whereas for SABR it was 87.4% (p = 0.0002). Liver volume receiving ≥15 Gy was similar for BA and SABR (278 vs. 256 cc, p = 0.3). Small bowel mean dose, as percent prescription dose, was higher for BA (10.8% vs. 7.1%, p = 0.006). Stomach mean dose was similar (4.9% vs. 4.8% of prescription dose, p = 0.98). Right kidney mean dose was greater for BA (6.7% vs. 4.2%, p = 0.07). BA leads to a higher target dose, similar dose to organs at risk, but potentially with lower target coverage compared with SABR. Further work is needed to determine ideal suitability for mono vs. combination therapy with this approach.

Interest in integrating magnetic resonance imaging (MRI) in radiation therapy (RT) practice has increased dramatically in recent years owing to its unique advantages such as excellent soft tissue contrast and capability of measuring biological properties. Continuous real-time imaging for intrafractional motion tracking without ionizing radiation serves as a particularly attractive feature for applications in RT. Despite its many advantages, the integration of MRI in RT workflows is not straightforward, with many unmet needs. MR safety remains one of the key challenges and concerns in the clinical implementation of MR simulators and MR-guided radiation therapy systems in radiation oncology. Most RT staff are not accustomed to working in an environment with a strong magnetic field. There are specific requirements in RT that are different from diagnostic applications. A large variety of implants and devices used in routine RT practice do not have clear MR safety labels. RT-specific imaging pulse sequences focusing on fast acquisition, high spatial integrity, and continuous, real-time acquisition require additional MR safety testing and evaluation. This article provides an overview of MR safety tailored toward RT staff, followed by discussions on specific requirements and challenges associated with MR safety in the RT environment. Strategies and techniques for developing an MR safety program specific to RT are presented and discussed.

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.