Visual analytics for the exploration of multiparametric cancer imaging

Tumor tissue characterization can play an important role in the diagnosis and design of effective treatment strategies. In order to gather and combine the necessary tissue information, multi-modal imaging is used to derive a number of parameters indicative of tissue properties. The exploration and analysis of relationships between parameters and, especially, of differences among distinct intra-tumor regions is particularly interesting for clinical researchers to individualize tumor treatment. However, due to high data dimensionality and complexity, the current clinical workflow is time demanding and does not provide the necessary intra-tumor insight. We implemented a new application for the exploration of the relationships between parameters and heterogeneity within tumors. In our approach, we employ a well-known dimensionality reduction technique [5] to map the high-dimensional space of tissue properties into a 2D information space that can be interactively explored with integrated information visualization techniques. We conducted several usage scenarios with real-patient data, of which we present a case of advanced cervical cancer. First indications show that our application introduces new features and functionalities that are not available within the current clinical approach

Bladder runner:visual analytics for the exploration of RT-induced bladder toxicity in a cohort study

\u3cp\u3eWe present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT-induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder – due to differences in urinary filling – causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose-Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter-patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT-induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.\u3c/p\u3

Visual analytics for the exploration of tumor tissue characteristics

Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra-tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non-invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging-derived feature space is prohibiting for easy exploration and analysis - especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging-derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging-derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra-tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients

A fluoroscopy-based planning and guidance software tool for minimally invasive hip refixation by cement injection

\u3cp\u3ePurpose: In orthopaedics, minimally invasive injection of bone cement is an established technique. We present HipRFX, a software tool for planning and guiding a cement injection procedure for stabilizing a loosening hip prosthesis. HipRFX works by analysing a pre-operative CT and intraoperative C-arm fluoroscopic images. Methods: HipRFX simulates the intraoperative fluoroscopic views that a surgeon would see on a display panel. Structures are rendered by modelling their X-ray attenuation. These are then compared to actual fluoroscopic images which allow cement volumes to be estimated. Five human cadaver legs were used to validate the software in conjunction with real percutaneous cement injection into artificially created periprothetic lesions. Results: Based on intraoperatively obtained fluoroscopic images, our software was able to estimate the cement volume that reached the pre-operatively planned targets. The actual median target lesion volume was 3.58 ml (range 3.17–4.64 ml). The median error in computed cement filling, as a percentage of target volume, was 5.3 % (range 2.2–14.8 %). Cement filling was between 17.6 and 55.4 % (median 51.8 %). Conclusions: As a proof of concept, HipRFX was capable of simulating intraoperative fluoroscopic C-arm images. Furthermore, it provided estimates of the fraction of injected cement deposited at its intended target location, as opposed to cement that leaked away. This level of knowledge is usually unavailable to the surgeon viewing a fluoroscopic image and may aid in evaluating the success of a percutaneous cement injection intervention.\u3c/p\u3