Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads

Characterization of Metabolic, Diffusion, and Perfusion Properties in GBM: Contrast-Enhancing versus Non-Enhancing Tumor.

BackgroundAlthough the contrast-enhancing (CE) lesion on T1-weighted MR images is widely used as a surrogate for glioblastoma (GBM), there are also non-enhancing regions of infiltrative tumor within the T2-weighted lesion, which elude radiologic detection. Because non-enhancing GBM (Enh-) challenges clinical patient management as latent disease, this study sought to characterize ex vivo metabolic profiles from Enh- and CE GBM (Enh+) samples, alongside histological and in vivo MR parameters, to assist in defining criteria for estimating total tumor burden.MethodsFifty-six patients with newly diagnosed GBM received a multi-parametric pre-surgical MR examination. Targets for obtaining image-guided tissue samples were defined based on in vivo parameters that were suspicious for tumor. The actual location from where tissue samples were obtained was recorded, and half of each sample was analyzed for histopathology while the other half was scanned using HR-MAS spectroscopy.ResultsThe Enh+ and Enh- tumor samples demonstrated comparable mitotic activity, but also significant heterogeneity in microvascular morphology. Ex vivo spectroscopic parameters indicated similar levels of total choline and N-acetylaspartate between these contrast-based radiographic subtypes of GBM, and characteristic differences in the levels of myo-inositol, creatine/phosphocreatine, and phosphoethanolamine. Analysis of in vivo parameters at the sample locations were consistent with histological and ex vivo metabolic data.ConclusionsThe similarity between ex vivo levels of choline and NAA, and between in vivo levels of choline, NAA and nADC in Enh+ and Enh- tumor, indicate that these parameters can be used in defining non-invasive metrics of total tumor burden for patients with GBM

Pathological perspectives in pilocytic astrocytomas: Extent of resection as the sole critical factor for recurrence-free survival, and the challenge of evaluating conclusions derived from limited data

Introduction: Pilocytic astrocytoma (PA) is one of the most common primary intracranial neoplasms in childhood with an overall favorable prognosis. Despite decades of experience, there are still diagnostic and treatment challenges and unresolved issues regarding risk factors associated with recurrence, most often due to conclusions of publications with limited data. We analyzed 499 patients with PA diagnosed in a single institution over 30 years in order to provide answers to some of the unresolved issues. Materials and Methods: We identified pilocytic astrocytomas diagnosed at the University of California, San Francisco, between 1989 and 2019, confirmed the diagnoses using the WHO 2021 essential and desirable criteria, and performed a retrospective review of the demographic and clinical features of the patients and the radiological, pathologic and molecular features of the tumors. Results: Among the patients identified from pathology archives, 499 cases fulfilled the inclusion criteria. Median age at presentation was 12 years (range 3.5 months – 73 years) and the median follow-up was 78.5 months. Tumors were predominantly located in the posterior fossa (52.6%). There were six deaths, but there were confounding factors that prevented a clear association of death to tumor progression. Extent of resection was the only significant factor for recurrence-free survival. Recurrence-free survival time was 321.0 months for gross total resection, compared to 160.9 months for subtotal resection (log rank, p <0.001). Conclusion: Multivariate analysis was able to identify extent of resection as the only significant variable to influence recurrence-free survival. We did not find a statistically significant association between age, NF1 status, tumor location, molecular alterations, and outcome. Smaller series with apparently significant results may have suffered from limited sample size, limited variables, acceptance of univariate analysis findings as well as a larger p value for biological significance. PA still remains a predominantly surgical disease and every attempt should be made to achieve gross total resection since this appears to be the most reliable predictor of recurrence-free survival

Pathological perspectives in pilocytic astrocytomas: Extent of resection as the sole critical factor for recurrence-free survival, and the challenge of evaluating conclusions derived from limited data

Introduction: Pilocytic astrocytoma (PA) is one of the most common primary intracranial neoplasms in childhood with an overall favorable prognosis. Despite decades of experience, there are still diagnostic and treatment challenges and unresolved issues regarding risk factors associated with recurrence, most often due to conclusions of publications with limited data. We analyzed 499 patients with PA diagnosed in a single institution over 30 years in order to provide answers to some of the unresolved issues. Materials and Methods: We identified pilocytic astrocytomas diagnosed at the University of California, San Francisco, between 1989 and 2019, confirmed the diagnoses using the WHO 2021 essential and desirable criteria, and performed a retrospective review of the demographic and clinical features of the patients and the radiological, pathologic and molecular features of the tumors. Results: Among the patients identified from pathology archives, 499 cases fulfilled the inclusion criteria. Median age at presentation was 12 years (range 3.5 months – 73 years) and the median follow-up was 78.5 months. Tumors were predominantly located in the posterior fossa (52.6%). There were six deaths, but there were confounding factors that prevented a clear association of death to tumor progression. Extent of resection was the only significant factor for recurrence-free survival. Recurrence-free survival time was 321.0 months for gross total resection, compared to 160.9 months for subtotal resection (log rank, p <0.001). Conclusion: Multivariate analysis was able to identify extent of resection as the only significant variable to influence recurrence-free survival. We did not find a statistically significant association between age, NF1 status, tumor location, molecular alterations, and outcome. Smaller series with apparently significant results may have suffered from limited sample size, limited variables, acceptance of univariate analysis findings as well as a larger p value for biological significance. PA still remains a predominantly surgical disease and every attempt should be made to achieve gross total resection since this appears to be the most reliable predictor of recurrence-free survival

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy