MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. RESULTS: The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION: Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

ScrollyVis: Interactive Visual Authoring of Guided Dynamic Narratives for Scientific Scrollytelling.

Visual stories are an effective and powerful tool to convey specific information to a diverse public. Scrollytelling is a recent visual storytelling technique extensively used on the web, where content appears or changes as users scroll up or down a page. By employing the familiar gesture of scrolling as its primary interaction mechanism, it provides users with a sense of control, exploration and discoverability while still offering a simple and intuitive interface. In this article, we present a novel approach for authoring, editing, and presenting data-driven scientific narratives using scrollytelling. Our method flexibly integrates common sources such as images, text, and video, but also supports more specialized visualization techniques such as interactive maps as well as scalar field and mesh data visualizations. We show that scrolling navigation can be used to traverse dynamic narratives and demonstrate how it can be combined with interactive parameter exploration. The resulting system consists of an extensible web-based authoring tool capable of exporting stand-alone stories that can be hosted on any web server. We demonstrate the power and utility of our approach with case studies from several diverse scientific fields and with a user study including 12 participants of diverse professional backgrounds. Furthermore, an expert in creating interactive articles assessed the usefulness of our approach and the quality of the created stories.

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.

Digital body preservation: Technique and applications.

High-fidelity anatomical models can be produced with three-dimensional (3D) scanning techniques and as such be digitally preserved, archived, and subsequently rendered through various media. Here, a novel methodology-digital body preservation-is presented for combining and matching scan geometry with radiographic imaging. The technique encompasses joining layers of 3D surface scans in an anatomical correct spatial relationship. To do so, a computed tomography (CT) volume is used as template to join and merge different surface scan geometries by means of nonrigid registration into a single environment. In addition, the use and applicability of the generated 3D models in digital learning modalities is presented. Finally, as computational expense is usually the main bottleneck in extended 3D applications, the influence of mesh simplification in combination with texture mapping on the quality of 3D models was investigated. The physical fidelity of the simplified meshes was evaluated in relation to their resolution and with respect to key anatomical features. Large- and medium-scale features were well preserved despite extensive 3D mesh simplification. Subtle fine-scale features, particular in curved areas demonstrated the major limitation to extensive mesh size reduction. Depending on the local topography, workable mesh sizes ranging from 10% to 3% of the original size could be obtained, making them usable in various learning applications and environments.

Interobserver agreement and prognostic impact for MRI-based 2018 FIGO staging parameters in uterine cervical cancer.

OBJECTIVES: To evaluate the interobserver agreement for MRI-based 2018 International Federation of Gynecology and Obstetrics (FIGO) staging parameters in patients with cervical cancer and assess the prognostic value of these MRI parameters in relation to other clinicopathological markers. METHODS: This retrospective study included 416 women with histologically confirmed cervical cancer who underwent pretreatment pelvic MRI from May 2002 to December 2017. Three radiologists independently recorded MRI-derived staging parameters incorporated in the 2018 FIGO staging system. Kappa coefficients (κ) for interobserver agreement were calculated. The predictive and prognostic values of the MRI parameters were explored using ROC analyses and Kaplan-Meier with log-rank tests, and analyzed in relation to clinicopathological patient characteristics. RESULTS: Overall agreement was substantial for the staging parameters: tumor size > 2 cm (κ = 0.80), tumor size > 4 cm (κ = 0.76), tumor size categories (≤ 2 cm; > 2 and ≤ 4 cm; > 4 cm) (κ = 0.78), parametrial invasion (κ = 0.63), vaginal invasion (κ = 0.61), and enlarged lymph nodes (κ = 0.63). Higher MRI-derived tumor size category (≤ 2 cm; > 2 and ≤ 4 cm; > 4 cm) was associated with a stepwise reduction in survival (p ≤ 0.001 for all). Tumor size > 4 cm and parametrial invasion at MRI were associated with aggressive clinicopathological features, and the incorporation of these MRI-based staging parameters improved risk stratification when compared to corresponding clinical assessments alone. CONCLUSION: The interobserver agreement for central MRI-derived 2018 FIGO staging parameters was substantial. MRI improved the identification of patients with aggressive clinicopathological features and poor survival, demonstrating the potential impact of MRI enabling better prognostication and treatment tailoring in cervical cancer. KEY POINTS: • The overall interobserver agreement was substantial (κ values 0.61-0.80) for central MRI staging parameters in the 2018 FIGO system. • Higher MRI-derived tumor size category was linked to a stepwise reduction in survival (p ≤ 0.001 for all). • MRI-derived tumor size > 4 cm and parametrial invasion were associated with aggressive clinicopathological features, and the incorporation of these MRI-derived staging parameters improved risk stratification when compared to clinical assessments alone.

Ten Open Challenges in Medical Visualization.

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.

The role of the longitudinal muscle in the anal sphincter complex: Implications for the Intersphincteric Plane in Low Rectal Cancer Surgery?

Intersphincteric resection (ISR) enables radical sphincter-preserving surgery in a subset of low rectal tumors impinging on the anal sphincter complex (ASC). Excellent anatomical knowledge is essential for optimal ISR. This study describes the role of the longitudinal muscle (LM) in the ASC and implications for ISR and other low rectal and anal pathologies. Six human adult en bloc cadaveric specimens (three males, three females) were obtained from the University of Leeds GIFT Research Tissue Programme. Paraffin-embedded mega blocks containing the ASC were produced and serially sectioned at 250 µm intervals. Whole mount microscopic sections were histologically stained and digitally scanned. The intersphincteric plane was shown to be potentially very variable. In some places adipose tissue is located between the external anal sphincter (EAS) and internal anal sphincter (IAS), whereas in others the LM interdigitates to obliterate the plane. Elsewhere the LM is (partly) absent with the intersphincteric plane lying on the IAS. The LM gave rise to the formation of the submucosae and corrugator ani muscles by penetrating the IAS and EAS. In four of six specimens, striated muscle fibers from the EAS curled around the distal IAS reaching the anal submucosa. The ASC formed a complex structure, varying between individuals with an inconstant LM affecting the potential location of the intersphincteric plane as well as a high degree of intermingling striated and smooth muscle fibers potentially further disrupting the plane. The complexity of identifying the correct pathological staging of low rectal cancer is also demonstrated. Clin. Anat. 33:567-577, 2020. © 2019 Wiley Periodicals, Inc.

Towards Advanced Interactive Visualization for Virtual Atlases.

An atlas is generally defined as a bound collection of tables, charts or illustrations describing a phenomenon. In an anatomical atlas for example, a collection of representative illustrations and text describes anatomy for the purpose of communicating anatomical knowledge. The atlas serves as reference frame for comparing and integrating data from different sources by spatially or semantically relating collections of drawings, imaging data, and/or text. In the field of medical image processing, atlas information is often constructed from a collection of regions of interest, which are based on medical images that are annotated by domain experts. Such an atlas may be employed, for example, for automatic segmentation of medical imaging data. The combination of interactive visualization techniques with atlas information opens up new possibilities for content creation, curation, and navigation in virtual atlases. With interactive visualization of atlas information, students are able to inspect and explore anatomical atlases in ways that were not possible with the traditional method of presenting anatomical atlases in book format, such as viewing the illustrations from other viewpoints. With advanced interaction techniques, it becomes possible to query the data that forms the basis for the atlas, thus empowering researchers to access a wealth of information in new ways. So far, atlas-based visualization has been employed mainly for medical education, as well as biological research. In this survey, we provide an overview of current digital biomedical atlas tasks and applications and summarize relevant visualization techniques. We discuss recent approaches for providing next-generation visual interfaces to navigate atlas data that go beyond common text-based search and hierarchical lists. Finally, we reflect on open challenges and opportunities for the next steps in interactive atlas visualization.

PelVis: Atlas-based Surgical Planning for Oncological Pelvic Surgery.

Due to the intricate relationship between the pelvic organs and vital structures, such as vessels and nerves, pelvic anatomy is often considered to be complex to comprehend. In oncological pelvic surgery, a trade-off has to be made between complete tumor resection and preserving function by preventing damage to the nerves. Damage to the autonomic nerves causes undesirable post-operative side-effects such as fecal and urinal incontinence, as well as sexual dysfunction in up to 80 percent of the cases. Since these autonomic nerves are not visible in pre-operative MRI scans or during surgery, avoiding nerve damage during such a surgical procedure becomes challenging. In this work, we present visualization methods to represent context, target, and risk structures for surgical planning. We employ distance-based and occlusion management techniques in an atlas-based surgical planning tool for oncological pelvic surgery. Patient-specific pre-operative MRI scans are registered to an atlas model that includes nerve information. Through several interactive linked views, the spatial relationships and distances between the organs, tumor and risk zones are visualized to improve understanding, while avoiding occlusion. In this way, the surgeon can examine surgically relevant structures and plan the procedure before going into the operating theater, thus raising awareness of the autonomic nerve zone regions and potentially reducing post-operative complications. Furthermore, we present the results of a domain expert evaluation with surgical oncologists that demonstrates the advantages of our approach.

Careful Dissection of the Distal Ureter Is Highly Important in Nerve-sparing Radical Pelvic Surgery: A 3D Reconstruction and Immunohistochemical Characterization of the Vesical Plexus.

OBJECTIVE: Radical hysterectomy with pelvic lymphadenectomy (RHL) is the preferred treatment for early-stage cervical cancer. Although oncological outcome is good with regard to recurrence and survival rates, it is well known that RHL might result in postoperative bladder impairments due to autonomic nerve disruption. The pelvic autonomic network has been extensively studied, but the anatomy of nerve fibers branching off the inferior hypogastric plexus to innervate the bladder is less known. Besides, the pathogenesis of bladder dysfunction after RHL is multifactorial but remains unclear. We studied the 3-dimensional anatomy and neuroanatomical composition of the vesical plexus and describe implications for RHL. MATERIALS AND METHODS: Six female adult cadaveric pelvises were macroscopically dissected. Additionally, a series of 10 female fetal pelvises (embryonic age, 10-22 weeks) was studied. Paraffin-embedded blocks were transversely sliced in 8-µm sections. (Immuno) histological analysis was performed with hematoxylin and eosin, azan, and antibodies against S-100 (Schwann cells), tyrosine hydroxylase (postganglionic sympathetic fibers), and vasoactive intestinal peptide (postganglionic parasympathetic fibers). The results were 3-dimensionally visualized. RESULTS: The vesical plexus formed a group of nerve fibers branching off the ventral part of the inferior hypogastric plexus to innervate the bladder. In all adult and fetal specimens, the vesical plexus was closely related to the distal ureter and located in both the superficial and deep layers of the vesicouterine ligament. Efferent nerve fibers belonging to the vesical plexus predominantly expressed tyrosine hydroxylase and little vasoactive intestinal peptide. CONCLUSIONS: The vesical plexus is located in both layers of the vesicouterine ligament and has a very close relationship with the distal ureter. Complete mobilization of the ureter in RHL might cause bladder dysfunction due to sympathetic and parasympathetic denervation. Hence, the distal ureter should be regarded as a risk zone in which the vesical plexus can be damaged.

Understanding Lymphatic Drainage Pathways of the Ovaries to Predict Sites for Sentinel Nodes in Ovarian Cancer.

OBJECTIVE: In ovarian cancer, detection of sentinel nodes is an upcoming procedure. Perioperative determination of the patient's sentinel node(s) might prevent a radical lymphadenectomy and associated morbidity. It is essential to understand the lymphatic drainage pathways of the ovaries, which are surprisingly up till now poorly investigated, to predict the anatomical regions where sentinel nodes can be found. We aimed to describe the lymphatic drainage pathways of the human ovaries including their compartmental fascia borders. METHODS: A series of 3 human female fetuses and tissues samples from 1 human cadaveric specimen were studied. Immunohistochemical analysis was performed on paraffin-embedded transverse sections (8 or 10 µm) using antibodies against Lyve-1, S100, and α-smooth muscle actin to identify the lymphatic endothelium, Schwann, and smooth muscle cells, respectively. Three-dimensional reconstructions were created. RESULTS: Two major and 1 minor lymphatic drainage pathways from the ovaries were detected. One pathway drained via the proper ligament of the ovaries (ovarian ligament) toward the lymph nodes in the obturator fossa and the internal iliac artery. Another pathway drained the ovaries via the suspensory ligament (infundibulopelvic ligament) toward the para-aortic and paracaval lymph nodes. A third minor pathway drained the ovaries via the round ligament to the inguinal lymph nodes. Lymph vessels draining the fallopian tube all followed the lymphatic drainage pathways of the ovaries. CONCLUSIONS: The lymphatic drainage pathways of the ovaries invariably run via the suspensory ligament (infundibulopelvic ligament) and the proper ligament of the ovaries (ovarian ligament), as well as through the round ligament of the uterus. Because ovarian cancer might spread lymphogenously via these routes, the sentinel node can be detected in the para-aortic and paracaval regions, obturator fossa and surrounding internal iliac arteries, and inguinal regions. These findings support the strategy of injecting tracers in both ovarian ligaments to identify sentinel nodes.