Radiation Therapy for Colorectal Liver Metastasis: The Effect of Radiation Therapy Dose and Chemotherapy on Local Control and Survival.

Purpose: Colorectal liver metastases (CLMs) represent a radioresistant histology. We aimed to investigate CLM radiation therapy (RT) outcomes and explore the association with treatment parameters. Methods and Materials: This retrospective analysis of CLM treated with RT at Memorial Sloan Kettering Cancer Center used Kaplan-Meier analysis to estimate freedom from local progression (FFLP), hepatic progression-free, progression-free, and overall survival (OS). Cox proportional hazards regression was used to evaluate association with clinical factors. Dose-response relationship was further evaluated using a mechanistic tumor control probability (TCP) model. Results: Ninety patients with 122 evaluable CLMs treated 2006 to 2019 with a variety of RT fractionation schemes with a median biologically effective dose (α/ß = 10; BED10) of 97.9 Gy (range, 43.2-187.5 Gy) were included. Median lesion size was 3.5 cm (0.7-11.8 cm). Eighty-seven patients (97%) received prior systemic therapy, and 73 patients (81%) received prior liver-directed therapy. At a median follow-up of 26.4 months, rates of FFLP and OS were 62% (95% CI, 53%-72%) and 75% (66%-84%) at 1 year and 42% (95% CI, 32%-55%) and 44% (95% CI, 34%-57%) at 2 years, respectively. BED10 below 96 Gy and receipt of ≥3 lines of chemotherapy were associated with worse FFLP (hazard ratio [HR], 2.69; 95% CI, 1.54-4.68; P < .001 and HR, 2.67; 95% CI, 1.50-4.74; P < .001, respectively) and OS (HR, 2.35; 95% CI, 1.35-4.09; P = .002 and HR, 4.70; 95% CI, 2.37-9.31; P < .001) on univariate analyses, which remained significant or marginally significant on multivariate analyses. A mechanistic Tumor Control Probability (TCP) model showed a higher 2-Gy equivalent dose needed for local control in patients who had been exposed to ≥ 3 lines of chemotherapy versus 0 to 2 (250 ± 29 vs 185 ± 77 Gy for 70% TCP). Conclusions: In a large single-institution series of heavily pretreated patients with CLM undergoing liver RT, low BED10 and multiple prior lines of systemic therapy were associated with lower local control and OS. These results support continued dose escalation efforts for patients with CLM.

Simulating the Potential of Model-Based Individualized Prescriptions for Ultracentral Lung Tumors.

Purpose: The use of stereotactic body radiation therapy for ultracentral lung tumors is limited by increased toxicity. We hypothesized that using published normal tissue complication probability (NTCP) and tumor control probability (TCP) models could improve the therapeutic ratio between tumor control and toxicity. A proposed model-based approach was applied to virtually replan early-stage non-small cell lung cancer (NSCLC) tumors. Methods and Materials: The analysis included 63 patients with ultracentral NSCLC tumors treated at our center between 2008 and 2017. Along with current clinical constraints, additional NTCP model-based criteria, including for grade 3+ radiation pneumonitis (RP3+) and grade 2+ esophagitis, were implemented using 4 different fractionation schemes. Scaled dose distributions resulting in the highest TCP without violating constraints were selected (optimal plan [Planopt]). Planopt predictions were compared with the observed local control and toxicities. Results: The observed 2-year local control rate was 72% (95% CI, 57%-88%) compared with 87% (range, 6%-93%) for Planopt TCP. Thirty-nine patients had Planopt with TCP > 80%, and 14 patients had Planopt TCP < 50%. The Planopt NTCPs for RP3+ were reduced by nearly half compared with patients' observed RP3+. The RP3+ NTCP was the most frequent reason for TCP of Planopt < 80% (14/24 patients), followed by grade 2+ esophagitis NTCP (5/24 patients) due to larger tumors (>40 cc vs ≤40 cc; P = .002) or a shorter tumor to esophagus distance (≥5 cm vs <5 cm; P < .001). Conclusions: We demonstrated the potential for model-based prescriptions to yield higher TCP while respecting NTCP for patients with ultracentral NSCLC. Individualizing treatments based on NTCP- and TCP-driven simulations halved the predicted relative to the observed rates of RP3+. Our simulations also identified patients whose TCP could not be improved without violating NTCP due to larger tumors or a near tumor to esophagus proximity.

ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions.

BACKGROUND AND OBJECTIVES: Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves. METHODS: We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts. CONCLUSION: ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.

External validation of pulmonary radiotherapy toxicity models for ultracentral lung tumors.

Introduction: Pulmonary toxicity is dose-limiting in stereotactic body radiation therapy (SBRT) for tumors that abut the proximal bronchial tree (PBT), esophagus, or other mediastinal structures. In this work we explored published models of pulmonary toxicity following SBRT for such ultracentral tumors in an independent cohort of patients. Methods: The PubMed database was searched for pulmonary toxicity models. Identified models were tested in a cohort of patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60 % primary and 40 % metastatic tumors treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx prescribed as 100 % dose to PTV. Results: Seven published NTCP models from two studies were identified. The NTCP models utilized PBT max point dose (Dmax), D0.2 cm3, V65, V100, and V130. Within the independent cohort, the ≥ grade 3 toxicity and grade 5 toxicity rates were 18 % and 7-10 %, respectively, and the Dmax models best described pulmonary toxicity. The Dmax to 0.1 cm3 model was better calibrated and had increased steepness compared to the Dmax model. A re-planning study minimizing PBT 0.1 cm3 to below 122 Gy in EQD23 (for a 10 % ≥grade 3 pulmonary toxicity) was demonstrated to be completely feasible in 4/6 patients, and dose to PBT 0.1 cm3 was considerably lowered in all six patients. Conclusions: Pulmonary toxicity models were identified from two studies and explored within an independent ultracentral lung tumor cohort. A modified Dmax to 0.1 cm3 PBT model displayed the best performance. This model could be utilized as a starting point for rationally constructed airways constraints in ultracentral patients treated with SBRT or hypofractionation.

Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.

Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62-0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93-2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05-6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56-0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.

Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. METHODS: The key idea of our approach called cross-modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training to extract more informative features during training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDNs) using unpaired CBCT and MRI datasets. UDA approach uses CBCT and MRI that are not aligned and may arise from different sets of patients. The UDA network synthesizes pseudo MRI from CBCT images. The SDN consists of teacher MRI and student CBCT segmentation networks. Feature distillation regularizes the student network to extract CBCT features that match the statistical distribution of MRI features extracted by the teacher network and obtain better differentiation of tumor from background. The UDA network was implemented with a cycleGAN improved with contextual losses separately on Unet and dense fully convolutional segmentation networks (DenseFCN). Performance comparisons were done against CBCT only using 2D and 3D networks. We also compared against an alternative framework that used UDA with MR segmentation network, whereby segmentation was done on the synthesized pseudo MRI representation. All networks were trained with 216 weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different patient cohorts. Validation was done on 20 weekly CBCTs from patients not used in training. Independent testing was done on 38 weekly CBCTs from patients not used in training or validation. Segmentation accuracy was measured using surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th percentile (HD95) metrics. RESULTS: The CMEDL approach significantly improved (p < 0.001) the accuracy of both Unet (SDSC of 0.83 ± 0.08; HD95 of 7.69 ± 7.86 mm) and DenseFCN (SDSC of 0.75 ± 0.13; HD95 of 11.42 ± 9.87 mm) over CBCT only 2DUnet (SDSC of 0.69 ± 0.11; HD95 of 21.70 ± 16.34 mm), 3D Unet (SDSC of 0.72 ± 0.20; HD95 15.01 ± 12.98 mm), and DenseFCN (SDSC of 0.66 ± 0.15; HD95 of 22.15 ± 17.19 mm) networks. The alternate framework using UDA with the MRI network was also more accurate than the CBCT only methods but less accurate the CMEDL approach. CONCLUSIONS: Our results demonstrate feasibility of the introduced CMEDL approach to produce reasonably accurate lung cancer segmentation from CBCT images. Further validation on larger datasets is necessary for clinical translation.

Salvage of locally recurrent breast cancer with repeat breast conservation using 45 Gy hyperfractionated partial breast re-irradiation.

PURPOSE: Mastectomy has long been the preferred approach for local salvage of recurrent breast cancer following breast-conservation therapy (BCT). Growing interest in avoiding mastectomy prompted RTOG 1014, a landmark phase two study demonstrating the feasibility of repeat BCT using a novel radiotherapy (RT) regimen (i.e., 45 Gy administered in 30 fractions of 1.5 Gy twice-daily to the partial breast, "rePBI"). We adopted this regimen as our institutional standard and report our observations regarding the safety and efficacy of rePBI as salvage therapy. METHODS: All patients at our institution who underwent repeat BCT and subsequently received rePBI from 2011 to 2019 were identified. Clinicopathologic features and treatment characteristics for both primary breast cancers and recurrences were collected, as were rates of subsequent recurrence and treatment-associated toxicities. RESULTS: The cohort included 34 patients with a median age of 65.8 (46.2-78.2) at the time of rePBI. At a median follow-up of 23.5 months, there were two subsequent locoregional recurrences (2-year local control rate 97%). There was no grade ≥ 3 toxicity. The most common acute toxicity (< 3 months) was radiation dermatitis (100%), and common grade 1-2 late toxicities (> 3 months) included fibrosis in 14 (41%), breast asymmetry in 12 (35%), and chest wall pain in 11 (32%). CONCLUSIONS: Repeat breast conservation using the hyperfractionated partial breast RT regimen defined by RTOG 1014 (45 Gy administered in 30 1.5 Gy twice-daily fractions) appears effective and well tolerated. No grade 3 or higher toxicities were observed and local control was excellent. Longer term follow-up among larger cohorts will define whether salvage mastectomy should remain the preferred standard.

Management of ipsilateral breast tumor recurrence following breast conservation surgery: a comparative study of re-conservation vs mastectomy.

BACKGROUND: Breast conservation therapy (BCT) is well established for the management of primary operable breast cancer, with oncologic outcomes comparable to those of mastectomy. It remains unclear whether re-conservation therapy (RCT) is suitable for those patients who develop ipsilateral breast tumor recurrence (IBTR), for whom mastectomy is generally recommended. METHODS: We identified women who underwent BCT for invasive or ductal carcinoma in situ and developed IBTR as a first event, comparing the pattern of subsequent events and survival for those treated by RCT versus mastectomy. RESULTS: Of 16,968 patents who had BCT, 322 (1.9%) developed an isolated IBTR as a first event between 1999 and 2019. 130 (40%) had RCT and 192 (60%) mastectomy. Compared to mastectomy, the RCT patients were older (66 vs 53, < 0.001), had a longer disease-free interval (DFI: 5.8 vs 2.7 years (p < 0.001)), were less likely to have received RT (p < 0.001), endocrine therapy (ET) (p < 0.005) or combined RT/ET (< 0.001) as initial treatment, but the characteristics of their initial primary cancers and of their IBTR were comparable. At a median follow-up of 10.7 years following initial BCT and 6.5 years following IBTR, there were no differences in BCSS or OS between RCT and mastectomy. CONCLUSION: For BCT patients who developed IBTR as a first event, we observed comparable BCSS and OS from time of initial treatment and from time of IBTR, whether treated by RCT or mastectomy. These results support wider consideration of RCT in the management of IBTR, especially in the setting of older age and longer DFI.

Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy.

PURPOSE: Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. METHODS AND MATERIALS: Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). RESULTS: Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). CONCLUSIONS: A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Childhood tonsillectomy alters the primary distribution of HPV-related oropharyngeal squamous cell carcinoma.

OBJECTIVES: We investigated how tonsillectomy during childhood may influence the distribution of human papillomavirus (HPV) positive cancer of the tonsils in adult life using p16 as a surrogate marker for HPV infection. STUDY DESIGN: Retrospective observational study. METHODS: A total of 280 patients diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) and known p16 status were eligible for this study. Each participant was called to obtain the childhood tonsillectomy history. Respondents were subgrouped by p16 status and the primary tumor location. Patient demographic and clinical information was analyzed for association with Fisher's exact and Wilcoxon rank sum tests. Location of tumor was modeled using univariate (UVA) and multivariate (MVA) logistic regression with associated odds ratios (OR) and 95% confidence intervals. RESULTS: Of the 280 patients, 115 (41%) were respondents: 104 (90.4%) were p16 positive and 11 (9.6%) were p16 negative. For p16 positive patients, we observed a majority (93%) of intact tonsils in those with tonsil cancer, compared to 45% of intact tonsils in patients with p16 positive cancer elsewhere in the oropharynx (P < .001). MVA logistic regression showed that female gender (OR = 4.16, P = .0675), prior smoking history (OR = 2.6, P = .0367), and intact tonsils (OR = 15.2, P < .0001) were associated with tonsillar OPSCC. CONCLUSION: We found that patients with p16 positive OPSCC at a non-tonsil site were much more likely to have had prior tonsillectomy vs those with p16 positive OPSCC arising within the tonsil. Nevertheless, we do not advocate tonsillectomies as a public health policy to reduce HPV-related OPSCC. LEVEL OF EVIDENCE: 6.

Disparity in Outcomes for Adolescent and Young Adult Patients Diagnosed With Pediatric Solid Tumors Across 4 Decades.

OBJECTIVE: Cancer mortality is a leading cause of disease-related death in the adolescent and young adult (AYA) population. Compared with older and younger patients, AYA patients often experience worse cancer-specific outcomes. Here, we compare AYA and pediatric overall survival (OS) in the most common pediatric extracranial solid tumors. MATERIALS AND METHODS: Using the US Surveillance, Epidemiology, and End Results database, we studied patients (age, 0 to 39 y) diagnosed with Ewing sarcoma, neuroblastoma, osteosarcoma, rhabdomyosarcoma, and Wilms tumor. RESULTS: A total of 12,375 patients (age, 0 to 39 y) were diagnosed between 1973 and 2010 (8247 pediatric and 4128 AYA patients). AYA patients with rhabdomyosarcoma and Ewing sarcoma were more likely to present with metastatic disease. OS was significantly worse in the AYA cohort for all tumor types (P<0.001) with the exception of osteosarcoma (P=0.29). Across 2 treatment time periods (1973 to 1989 and 1990 to 2010), there was significant improvement in 5-year OS in all tumor types with the exception of rhabdomyosarcoma; however, AYA patients continued to experience worse OS in the modern treatment cohort with the exception of osteosarcoma patients. There was no improvement in OS among AYA patients with Ewing sarcoma, neuroblastoma, rhabdomyosarcoma, or Wilms tumor over the 2 treatment eras. CONCLUSIONS: For the most common pediatric extracranial solid tumors, AYA patients experience significantly worse OS compared with pediatric patients. Although improvements in therapy have led to gain in survival for pediatric patients, with the exception of osteosarcoma, AYA experienced no increase in survival over the study period. This investigation demonstrates the importance for further research in the AYA population.

Assessment of the treatment approach and survival outcomes in a modern cohort of patients with atypical teratoid rhabdoid tumors using the National Cancer Database.

BACKGROUND: Atypical teratoid rhabdoid tumors (ATRTs) are rare brain tumors that occur primarily in children under the age of 3 years. This report evaluates the treatment approach and survival outcomes in a large cohort of patients treated in the United States. METHODS: Using the National Cancer Database, the analysis included all ATRT patients aged 0 to 18 years who were diagnosed between 2004 and 2012 and had complete treatment data. RESULTS: Three hundred sixty-one ATRT patients were evaluated. The 5-year overall survival (OS) rate was 29.9%, and it was significantly lower for children who were less than 3 years old (5-year OS, 27.7%) versus those who were 3 years old or older (5-year OS, 37.5%; P < .001). The best outcome was seen for patients with localized disease who received trimodality therapy (surgery, chemotherapy, and radiation therapy [RT]) with a 5-year OS rate of 46.8%. The utilization of trimodality therapy significantly increased during the study period (27.7% in 2004-2008 vs 45.1% in 2009-2012; P < .01), largely because of the increased use of RT. In a multivariate analysis, treatment that did not utilize trimodality therapy was associated with significantly worse OS (hazard ratio, 2.52; 95% confidence interval (1.82-3.51). Children aged 0 to 2 years were significantly less likely to receive trimodality therapy because of decreased utilization of RT in this age group. CONCLUSIONS: The use of trimodality therapy significantly increased during the study period and was associated with improved outcomes. For patients with localized disease who received trimodality therapy, the OS rate at 5 years approached 50%. However, further research into the optimal management of children less than 3 years old is needed because of their significantly worse OS in comparison with older children. Cancer 2017;123:682-687. © 2016 American Cancer Society.

Concurrent Ramsay Hunt syndrome and disseminated herpes zoster in a patient with relapsed chronic lymphocytic leukemia.

Ramsay Hunt Syndrome (RHS) is a rare complication of latent varicella-zoster virus (VZV) infection that can occur in immunocompetent host. It usually involves ipsilateral facial paralysis, ear pain and facial vesicles. Disseminated herpes zoster is another complication of VZV infection typically seen in immunocompromised hosts. We describe a patient with relapsed chronic lymphocytic leukemia (CLL) who presented simultaneously with RHS and disseminated herpes zoster. While other complications have been documented to coexist with RHS, to our knowledge, this is the first reported case in the literature of concurrent RHS with disseminated herpes zoster.

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases.

PURPOSE: Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases. METHODS: Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients. RESULTS: The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm. CONCLUSIONS: Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

A CDC20-APC/SOX2 Signaling Axis Regulates Human Glioblastoma Stem-like Cells.

Glioblastoma harbors a dynamic subpopulation of glioblastoma stem-like cells (GSCs) that can propagate tumors in vivo and is resistant to standard chemoradiation. Identification of the cell-intrinsic mechanisms governing this clinically important cell state may lead to the discovery of therapeutic strategies for this challenging malignancy. Here, we demonstrate that the mitotic E3 ubiquitin ligase CDC20-anaphase-promoting complex (CDC20-APC) drives invasiveness and self-renewal in patient tumor-derived GSCs. Moreover, CDC20 knockdown inhibited and CDC20 overexpression increased the ability of human GSCs to generate brain tumors in an orthotopic xenograft model in vivo. CDC20-APC control of GSC invasion and self-renewal operates through pluripotency-related transcription factor SOX2. Our results identify a CDC20-APC/SOX2 signaling axis that controls key biological properties of GSCs, with implications for CDC20-APC-targeted strategies in the treatment of glioblastoma.

Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction.

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework's accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework.

Toward a preoperative planning tool for brain tumor resection therapies.

BACKGROUND: Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures. METHODS: In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study. RESULTS: The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods. CONCLUSIONS: This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.

Intraoperative brain shift compensation: accounting for dural septa.

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.