Ahmed Hosny

Computational Radiomics System to Decode the Radiographic Phenotype

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.

Unlocking vendor-specific tags: Three-dimensional printing of echocardiographic data sets

From the Computational Imaging and Bioinformatics Laboratory, Department of Radiation Oncology, Dana-Farber Cancer Institute, and the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Mass. Disclosures: Authors have nothing to disclose with regard to commercial support. The Web site and online resources will remain as freeware. The authors have not and will not make any financial gains through the use of materials presented in this article. Received for publication June 7, 2017; revisions received Aug 11, 2017; accepted for publication Aug 23, 2017; available ahead of print Sept 21, 2017. Address for reprints: Joshua D. Dilley, MD, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 (E-mail: joshuaddilley@gmail.com). J Thorac Cardiovasc Surg 2018;155:143-5 0022-5223/

Artificial intelligence in radiology

36.00 Copyright 2017 by The American Association for Thoracic Surgery http://dx.doi.org/10.1016/j.jtcvs.2017.08.064 Three-dimensional rendering of a normal aortic valve in midsystole.

Robotic Bead Rolling

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.In this Opinion article, Hosny et al. discuss the application of artificial intelligence to image-based tasks in the field of radiology and consider the advantages and challenges of its clinical implementation.

Making data matter: Voxel printing for the digital fabrication of data across scales and domains

The robotic workflow proposed analyzes the bead rolling process, its potential digital interpretation, and improved fabrication aspects that accompany such a translation. For this process, a robotic tool has been developed that integrates multiple variables observed from existing bead rolling machines, while simultaneously allowing further control. Material-informed decisions required a series of tests evaluating optimum tool and workflow design. While the process provokes a multitude of potentialities, it has been put towards a structural behavior testing scenario to demonstrate its validity. It attempts to bridge analysis methods with prototyping as means of direct performance testing and evaluation. Deeply rooted within a parametric modeling environment, the workflow creates a single digital interface that links several platforms that otherwise are not in direct communication.

3D printing and intraoperative neuronavigation tailoring for skull base reconstruction after extended endoscopic endonasal surgery: proof of concept

Voxel-based 3D printing bridges the gap between digital information representation and physical material composition. We present a multimaterial voxel-printing method that enables the physical visualization of data sets commonly associated with scientific imaging. Leveraging voxel-based control of multimaterial three-dimensional (3D) printing, our method enables additive manufacturing of discontinuous data types such as point cloud data, curve and graph data, image-based data, and volumetric data. By converting data sets into dithered material deposition descriptions, through modifications to rasterization processes, we demonstrate that data sets frequently visualized on screen can be converted into physical, materially heterogeneous objects. Our approach alleviates the need to postprocess data sets to boundary representations, preventing alteration of data and loss of information in the produced physicalizations. Therefore, it bridges the gap between digital information representation and physical material composition. We evaluate the visual characteristics and features of our method, assess its relevance and applicability in the production of physical visualizations, and detail the conversion of data sets for multimaterial 3D printing. We conclude with exemplary 3D-printed data sets produced by our method pointing toward potential applications across scales, disciplines, and problem domains.

Data Analysis Strategies in Medical Imaging

In BriefA custom implantable device was designed to help reconstruct the skull base after extended endoscopic endonasal approaches. These implants are based on the specific anatomy of the patient, with tailored modifications to facilitate deployment and increase stability and efficacy. Their use will help decrease the incidence of cerebrospinal fluid leakage after endoscopic surgery, reducing returns to the operating room and length of hospital stay as well as improving overall safety for patients.

Use of a 3D-Printed Abdominal Compression Device to Facilitate CT Fluoroscopy–Guided Percutaneous Interventions

Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has allowed for detailed quantification of radiographic characteristics of tissues using predefined engineered algorithms or deep learning methods. Precedents in radiology as well as a wealth of research studies hint at the clinical relevance of these characteristics. However, critical challenges are associated with the analysis of medical imaging data. Although some of these challenges are specific to the imaging field, many others like reproducibility and batch effects are generic and have already been addressed in other quantitative fields such as genomics. Here, we identify these pitfalls and provide recommendations for analysis strategies of medical imaging data, including data normalization, development of robust models, and rigorous statistical analyses. Adhering to these recommendations will not only improve analysis quality but also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources. Clin Cancer Res; 24(15); 3492–9. ©2018 AACR.

A Novel Mixed Reality Navigation System for Laparoscopy Surgery.

OBJECTIVE The purpose of this article is to describe a handheld external compression device used to facilitate CT fluoroscopy-guided percutaneous interventions in the abdomen. CONCLUSION The device was designed with computer-aided design software to modify an existing gastrointestinal fluoroscopy compression device and was constructed by 3D printing. This abdominal compression device facilitates access to interventional targets, and its use minimizes radiation exposure of radiologists. Twenty-one procedures, including biopsies, drainage procedures, and an ablation, were performed with the device. Radiation dosimetry data were collected during two procedures.