Emerging Technologies, Signal Processing and Statistical Methods for Screening of Cervical Cancer In Vivo: Are They Good Candidates for Cervical Screening?

The current cervical cancer screening test (the Pap smear) is a manual cytological procedure. This cytology test has various limitations and many errors. Excellent candidates for improving the performance of the cervical cancer screening procedure are electro-optical systems (EOSs), used for assessment of the cervical cancer precursors in vivo, such as digital spectroscopy, digital colposcopy and bioelectrical phenomena-based systems. These EOSs use the advantages of signal processing methods and can replace the qualitative assessments, with objective metrics. The EOSs can be used as an adjunct to the current screener or as a primary screener. We analyse and discuss the effectiveness of the signal processing and statistical methods for diagnosis of cervical cancer in vivo. This analysis is reinforced by the presentation of the scientific and clinical contributions of these methods in clinical practice. As a result of this analysis, we outline and discuss the well-established estimates of the signal processing features and the ambiguous features, that are used for classification of the cervical pre-cancer in vivo

Computer-Aided Detection of Pathologically Enlarged Lymph Nodes On Non-Contrast CT In Cervical Cancer Patients For Low-Resource Settings

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily CT images, which are widely available in LMICs(1). A region of interest which is likely to contain the nodal volumes relevant for cervical cancer was defined on a single patient CT(2). This region was deformed onto new patients using an in-house, demons-based deformation software. Edge detection and erosion filtering were used to distinguish potential positive nodes from normal structures. Regions on adjacent slices were then connected into a potential nodal 3D-structure. To differentiate these 3D structures from normal tissues, eighty-six features were generated based on the shape and mean pixel values of the structures, and four classification ensemble methods were tested to differentiate the positive nodes from normal tissues. A cohort of fifty-eight MD Anderson cervical cancer patients with pathologically enlarged lymph nodes were used as a training-test set. Similarly, twenty MD Anderson cervical cancer patients were obtained as a validation set. They contained 154 and 35 pathologically enlarged lymph nodes, respectively. Model comparison led to the selection of the Adaboost ensemble model, utilizing 17 features. In the validation set, 60% of the clinically significant positive cervical cancer nodes were identified along with a false/true positive ratio of ~4:1. The entire process takes approximately 10/number-of-cores-minutes. Our findings demonstrated that our computer-aided detection model can assist in the identification of metastatic nodal disease where high quality diagnostic imaging is not readily available. By identifying these nodes, radiation treatment fields can be modified to include pathologically enlarged lymph nodes, which is an essential element to providing potentially curative radiotherapy for cervical cancer

Medical image analysis for simplified ultrasound protocols

Ultrasound is an imaging tool used in obstetrics to identify high-risk pregnancies. However, ultrasound (US) requires a trained operator, who guides a transducer in response to real-time interpretation of video content. In low- and middle-income countries (LMICs), there is a shortage of trained sonographers. In this thesis, we address this key challenge by combining simple US video sweeps with computational algorithms to provide clinical benefit. The sweeps can be taken by an US novice. First, we design an algorithm that automatically creates an assistive video overlay from a simple video sweep. The overlay assists interpretation of US video to assess placenta location. We describe the design and evaluation of a deep learning-based automatic segmentation model and a statistical data visualisation of 2-D placenta shapes. The data visualisation reveals the spectrum of placenta shapes in this problem space. A probabilistic graphical model is used to improve segmentations with regards to the highly variable placenta shape. From the automatic segmentations, image guidance is created, translating the clinical criteria into assistive visual information. Second, we explore analysis of multiple video sweeps using graphs. A three-node graph models three video sweeps, where the nodes encode binary sequences representing the fetal head frame-level detection across all video frames in a sweep. To better characterise the sweeps, we perform a statistical analysis of large-scale manual annotations of video sweeps in our dataset. This reveals common patterns of frame-level anatomy occurrence for different video sweep trajectories. Particular insight is gained for patterns that correspond to fetal pose. In this regard, we build a graph convolutional network to automatically classify fetal presentation, using graphs that combine complementary video sweep information relating to fetal pose. Finally, we demonstrate the feasibility of placenta 3-D reconstruction using multiple video sweeps. We pose this challenging problem as spatio-temporal alignment of US video. We first temporally align video sweeps to represent video content at the same temporal scale. Then, we use affine transformations to spatially align images in temporally aligned video. The results in this chapter are exciting as they show the feasibility of placenta 3-D reconstruction in a simple US sweep system

Localisation of sentinel lymph nodes in patients with melanomas by planar lymphoscintigraphic and hybrid SPECT/CT imaging

BACKGROUND: The aim of the study was to assess the role ofplanar lymphoscintigraphy and fusion imaging of SPECT/CT in sentinellymph node (SLN) identification in patients with melanomas.MATERIAL AND METHODS: Planar and hybrid SPECT/low-doseCT lymphoscintigraphy were performed in 113 consecutive patientswith melanomas (59 men, 54 women, mean age 57.6 withrange 11–87 years, BMI 29.4 ± 12.5). The radiopharmaceuticalwas injected around the tumour (Group A, 59 patients), oraround the scar (Group B, 54 patients). Localisation of melanomas:head and neck 4, trunk 55, upper extremities 28, lowerextremities 26. Planar and SPECT/CT images were interpretedseparately by two nuclear medicine physicians. Abilities of thesetwo techniques to image SLN were compared.RESULTS: SLNs were detected on lymphoscintigraphy comprisingplanar and SPECT-CT images in 108 (95.6%) study patients;there was failure to detect SLNs in the remaining 5 (4.4%) patients. Planar images identified 253 SLNs in 100 (88.5%) pts,with a mean of 2.2 ± 1.7 (range 0–9 nodes) per patient. In theremaining 13 (11.5%) patients no SLNs were detected on planarimages. On SPECT-CT images, 334 hot nodes were detectedin 107 (94.7%) patients with a mean of 3.0 ± 2.1 (range 0–9)nodes per patient. In the remaining 6 (5.3%) patients, SPECT-CTwas negative.SPECT/CT visualised lymphatic drainage in 8 (7.1%) patientswith non-visualisation on planar imaging.CONCLUSIONS: In some patients with melanomas SPECT/CTimproves detection of sentinel lymph nodes. It can image nodesnot visible on planar scintigrams, exclude false positive uptakeand exactly localize SLNs.BACKGROUND: The aim of the study was to assess the role ofplanar lymphoscintigraphy and fusion imaging of SPECT/CT in sentinellymph node (SLN) identification in patients with melanomas.MATERIAL AND METHODS: Planar and hybrid SPECT/low-doseCT lymphoscintigraphy were performed in 113 consecutive patientswith melanomas (59 men, 54 women, mean age 57.6 withrange 11–87 years, BMI 29.4 ± 12.5). The radiopharmaceuticalwas injected around the tumour (Group A, 59 patients), oraround the scar (Group B, 54 patients). Localisation of melanomas:head and neck 4, trunk 55, upper extremities 28, lowerextremities 26. Planar and SPECT/CT images were interpretedseparately by two nuclear medicine physicians. Abilities of thesetwo techniques to image SLN were compared.RESULTS: SLNs were detected on lymphoscintigraphy comprisingplanar and SPECT-CT images in 108 (95.6%) study patients;there was failure to detect SLNs in the remaining 5 (4.4%) patients. Planar images identified 253 SLNs in 100 (88.5%) pts,with a mean of 2.2 ± 1.7 (range 0–9 nodes) per patient. In theremaining 13 (11.5%) patients no SLNs were detected on planarimages. On SPECT-CT images, 334 hot nodes were detectedin 107 (94.7%) patients with a mean of 3.0 ± 2.1 (range 0–9)nodes per patient. In the remaining 6 (5.3%) patients, SPECT-CTwas negative.SPECT/CT visualised lymphatic drainage in 8 (7.1%) patientswith non-visualisation on planar imaging.CONCLUSIONS: In some patients with melanomas SPECT/CTimproves detection of sentinel lymph nodes. It can image nodesnot visible on planar scintigrams, exclude false positive uptakeand exactly localize SLNs

Preterm Labor Predictors: Maternal Characteristics, Ultrasound Findings, Biomarker, and Artificial Intelligence

The identification of risk factors for preterm labor is an important predictor. The risk factors for preterm labor can be maternal characteristics, namely maternal obstetric history, maternal body mass index and weight gain, multiple pregnancy, maternal infections, periodontal disease, maternal vitamin D deficiency, and lifestyle. Nowadays, various accurate diagnostic methods have been developed to diagnose preterm labor, namely ultrasound (cervical length, cervical consistency, uterocervical angle, and fetal adrenal gland) and biomarkers (IL-6 and IL-8 in cervicovaginal fluid, Placental Alpha Microglobulin-1 (PAMG-1), and Insulin-Like Growth Factor Binding Protein-1 (IGFBP-1), Vascular Endothelial Growth Factor (VEGF), Placental Growth Factor (PGF), Soluble VEGF Receptor-1 (sFlt-1), High Mobility Group Box-1 (HMGB1), and calponin. Artificial Intelligence was developed to predict preterm labor, namely in the form of ultrasound software which is capable of detecting cervical funneling processes ranging from resembling the T, Y, V, and U-shaped. This software is expected to be easily used by general practitioners and obstetricians and gynecologists, especially those who work in rural areas.