Luke Macyszyn

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

BACKGROUND MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). METHODS One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. RESULTS Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. CONCLUSIONS By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

PURPOSE To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. MATERIALS AND METHODS Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. RESULTS The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. CONCLUSION Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.

Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients’ molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

BACKGROUND Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. OBJECTIVE To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. METHODS Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. RESULTS This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. CONCLUSION Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

Automated Tumor Volumetry Using Computer-Aided Image Segmentation

RATIONALE AND OBJECTIVES Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. MATERIALS AND METHODS A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. RESULTS Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0-5 rating scale where 5 indicated perfect segmentation. CONCLUSIONS The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation.

Relating Quality of Life to Glasgow Outcome Scale Health States

There has recently been a call for the adoption of comparative effectiveness research (CER) and related research approaches for studying traumatic brain injury (TBI). These methods allow researchers to compare the effectiveness of different therapies in producing patient-oriented outcomes of interest. Heretofore, the only measures by which to compare such therapies have been mortality and rate of poor outcome. Better comparisons can be made if parametric, preference-based quality-of-life (QOL) values are available for intermediate outcomes, such as those described by the Glasgow Outcome Scale Extended (GOSE). Our objective was therefore to determine QOL for the health states described by the GOSE. We interviewed community members at least 18 years of age using the standard gamble method to assess QOL for descriptions of GOSE scores of 2-7 derived from the structured interview. Linear regression analysis was also performed to assess the effect of age, gender, and years of education on QOL. One hundred and one participants between the ages of 18 and 83 were interviewed (mean age 40 ± 19 years), including 55 men and 46 women. Functional impairment and QOL showed a strong inverse relationship, as assessed by both linear regression and the Spearman rank order coefficient. No consistent effect or age, gender, or years of education was seen. As expected, QOL decreased with functional outcome as described by the GOSE. The results of this study will provide the groundwork for future groups seeking to apply CER methods to clinical studies of TBI.

Direct versus indirect revascularization procedures for moyamoya disease: a comparative effectiveness study.

OBJECTIVE Moyamoya disease (MMD) is a chronic cerebrovascular disease that can lead to devastating neurological outcomes. Surgical intervention is the definitive treatment, with direct, indirect, and combined revascularization procedures currently employed by surgeons. The optimal surgical approach, however, remains unclear. In this decision analysis, the authors compared the effectiveness of revascularization procedures in both adult and pediatric patients with MMD. METHODS A comprehensive literature search was performed for studies of MMD. Using complication and success rates from the literature, the authors constructed a decision analysis model for treatment using a direct and indirect revascularization technique. Utility values for the various outcomes and complications were extracted from the literature examining preferences in similar clinical conditions. Sensitivity analysis was performed. RESULTS A structured literature search yielded 33 studies involving 4197 cases. Cases were divided into adult and pediatric populations. These were further subdivided into 3 different treatment groups: indirect, direct, and combined revascularization procedures. In the pediatric population at 5- and 10-year follow-up, there was no significant difference between indirect and combination procedures, but both were superior to direct revascularization. In adults at 4-year follow-up, indirect was superior to direct revascularization. CONCLUSIONS In the absence of factors that dictate a specific approach, the present decision analysis suggests that direct revascularization procedures are inferior in terms of quality-adjusted life years in both adults at 4 years and children at 5 and 10 years postoperatively, respectively. These findings were statistically significant (p < 0.001 in all cases), suggesting that indirect and combination procedures may offer optimal results at long-term follow-up.

Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning.

BACKGROUND Advances in white matter tractography enhance neurosurgical planning and glioma resection, but white matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to regions of interest or fractional anisotropy values. OBJECTIVE To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration and provides a consistent, accurate method of fiber bundle identification. METHODS An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function. RESULTS The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest. CONCLUSION We report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections. ABBREVIATIONS AF, arcuate fasciculusDTI, diffusion tensor imagingIFOF, inferior fronto-occipital fasciculusILF, inferior longitudinal fasciculusROI, region of interestWM, white matter.

Implementation of a departmental picture archiving and communication system: a productivity and cost analysis.

BACKGROUND Digital radiology enhances productivity and results in long-term cost savings. However, the viewing, storage, and sharing of outside imaging studies on compact discs at ambulatory offices and hospitals pose a number of unique challenges to a surgeons efficiency and clinical workflow. OBJECTIVE To improve the efficiency and clinical workflow of an academic neurosurgical practice when evaluating patients with outside radiological studies. METHODS Open-source software and commercial hardware were used to design and implement a departmental picture archiving and communications system (PACS). RESULTS The implementation of a departmental PACS system significantly improved productivity and enhanced collaboration in a variety of clinical settings. Using published data on the rate of information technology problems associated with outside studies on compact discs, this system produced a cost savings ranging from

Amyloid-β–related angiitis: a report of 2 cases with unusual presentations

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