Two of the most important prognostic indicators for breast cancer are tumor size and extent of axillary lymph node involvement. Data on 24,740 cases recorded in the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute were used to evaluate the breast cancer survival experience in a representative sample of women from the United States. Actuarial (life table) methods were used to investigate the 5-year relative survival rates in cases with known operative/pathologic axillary lymph node status and primary tumor diameter. Survival rates varied from 45.5% for tumor diameters equal to or greater than 5 cm with positive axillary nodes to 96.3% for tumors less than 2 cm and with no involved nodes. The relation between tumor size and lymph node status was investigated in detail. Tumor diameter and lymph node status were found to act as independent but additive prognostic indicators. As tumor size increased, survival decreased regardless of lymph node status; and as lymph node involvement increased, survival status also decreased regardless of tumor size. A linear relation was found between tumor diameter and the percent of cases with positive lymph node involvement. The results of our analyses suggest that disease progression to distant sites does not occur exclusively via the axillary lymph nodes, but rather that lymph node status serves as an indicator of the tumor's ability to spread.

Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases

This book attempts to achieve a difficult goal: to teach statistics to the novice so as to impart a liking and understanding of statistics. The book is geared toward a medical audience, since most examples are from the medical literature. The structure of the book consists of the following elements in each chapter: a small number of statistical rules of thumb, followed by a nontechnical explanation, a demonstration of how to work through the mechanics of doing the statistical test in question, a summary, and sample problems to be solved by the reader. (The answers, with explanations, are provided in an appendix.) Although a variety of univariate statistics are included, certain topics that are important in medical research are not. For example, there is little or no discussion of multiple regression, life-table techniques, or pooling of studies. These omissions, especially of multiple regression, are unfortunate. The Primer was derived from

Primer of Biostatistics

Perivascular, subdural meningeal and choroid plexus macrophages are non-parenchymal macrophages that mediate immune responses at brain boundaries. Although the origin of parenchymal microglia has recently been elucidated, much less is known about the precursors, the underlying transcriptional program and the dynamics of the other macrophages in the central nervous system (CNS). It was assumed that they have a high turnover from blood-borne monocytes. However, using parabiosis and fate-mapping approaches in mice, we found that CNS macrophages arose from hematopoietic precursors during embryonic development and established stable populations, with the notable exception of choroid plexus macrophages, which had dual origins and a shorter life span. The generation of CNS macrophages relied on the transcription factor PU.1, whereas the MYB, BATF3 and NR4A1 transcription factors were not required.

Origin, fate and dynamics of macrophages at central nervous system interfaces.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21552

Artificial intelligence in cancer imaging: Clinical challenges and applications.

Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article.

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

Study results show that our MR imaging computer-aided diagnosis algorithm, with use of a combination of computer-extracted MR imaging kinetic and morphologic features, has the potential to be extended to two prognostic tasks: (a) classification of noninvasive (ductal carcinoma in situ) versus invasive (invasive ductal carcinoma [IDC]) lesions and (b) further classification of IDC lesions into lesions with positive lymph nodes (LNs) and lesions with negative LNs.

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Purpose: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)].

Methods: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier’s AUC performance.

Results: In the large U.S. data set, sample high performance results include, AUC0.632+=0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+=0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+=0.90 with interval [0.847;0.919], all using the MCMC-BANN.

Conclusions: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.

Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t -SNE

Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI

Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.

Method, system, software and medium for advanced intelligent image analysis and display of medical images and information

The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks?grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions?were investigated. Step-wise feature selection was conducted by three-class BANNs. Classification was performed with three-class BANNs using leave-one-lesion-out cross-validation to yield computer-estimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80???0.05, 0.78???0.05 and 0.62???0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.

/pdf/computerized-three-class-classification-of-mri-based-59jk4jxk9m.pdf

Neha Bhooshan

Papers

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t -SNE

Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI

Method, system, software and medium for advanced intelligent image analysis and display of medical images and information

Computerized three-class classification of MRI-based prognostic markers for breast cancer