Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.publishedVersio

Whole Slide Quantification of Stromal Lymphatic Vessel Distribution and Peritumoral Lymphatic Vessel Density in Early Invasive Cervical Cancer: A Method Description

Peritumoral Lymphatic Vessel Density (LVD) is considered to be a predictive marker for the presence of lymph node metastases in cervical cancer. However, when LVD quantification relies on conventional optical microscopy and the hot spot technique, interobserver variability is significant and yields inconsistent conclusions. In this work, we describe an original method that applies computed image analysis to whole slide scanned tissue sections following immunohistochemical lymphatic vessel staining. This procedure allows to determine an objective LVD quantification as well as the lymphatic vessel distribution and its heterogeneity within the stroma surrounding the invasive tumor bundles. The proposed technique can be useful to better characterize lymphatic vessel interactions with tumor cells and could potentially impact on prognosis and therapeutic decisions

Handheld vital microscopy for the identification of microcirculatory alterations in cervical intraepithelial neoplasia and cervical cancer

BackgroundNinety percent of cervical cancer (CC) diagnoses and deaths occur in low and middle-income countries (LMICs). Especially in these countries, where human and material resources are limited, there is a need for real-time screening methods that enable immediate treatment decisions (i.e., ‘see and treat’).ObjectiveTo evaluate whether handheld vital microscopy (HVM) enables real-time detection of microvascular alterations associated with cervical intraepithelial neoplasia (CIN) and CC.MethodsA cross-sectional study was conducted in an oncologic hospital and outpatient clinic, and included ten healthy controls, ten women with CIN, and ten women with CC. The microvasculature was assessed in four quadrants of the uterine cervix using HVM. The primary outcome was the presence of abnormal angioarchitecture (AA). Secondary outcomes included capillary loop density (CD), total vessel density (TVD), functional capillary density (FCD), and the proportion of perfused vessels (PPV).Results198 image sequences of the cervical microvasculature were recorded. Compared to healthy controls, significantly more abnormal image sequences were observed in women with high-grade CIN (11 % vs. 44 %, P < 0.001) and women with CC (11 % vs. 69 %, P < 0.001). TVD, FCD, and PPV were lower in women with CIN and CC.ConclusionsHVM enables easy, real-time, non-invasive assessment of cervical lesions through the detection of microvascular alterations. Thereby, HVM potentially provides an opportunity for point-of-care screening, which may enable immediate treatment decisions (see and treat) and reduce the number of unnecessary surgical interventions

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv