Rasu Shrestha

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application—Part 1: From Methodology to Clinical Implementation

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancements in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration have ushered us into the era of radiomics, which has tremendous potential in clinical decision support as well as drug discovery. There are important issues to consider to incorporate radiomics as a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that to enterprise development (Part 2).

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application—Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

Value-Based Assessment of Radiology Reporting Using Radiologist-Referring Physician Two-Way Feedback System—a Design Thinking-Based Approach

In the era of value-based healthcare, many aspects of medical care are being measured and assessed to improve quality and reduce costs. Radiology adds enormously to health care costs and is under pressure to adopt a more efficient system that incorporates essential metrics to assess its value and impact on outcomes. Most current systems tie radiologists’ incentives and evaluations to RVU-based productivity metrics and peer-review-based quality metrics. In a new potential model, a radiologist’s performance will have to increasingly depend on a number of parameters that define “value,” beginning with peer review metrics that include referrer satisfaction and feedback from radiologists to the referring physician that evaluates the potency and validity of clinical information provided for a given study. These new dimensions of value measurement will directly impact the cascade of further medical management. We share our continued experience with this project that had two components: RESP (Referrer Evaluation System Pilot) and FRACI (Feedback from Radiologist Addressing Confounding Issues), which were introduced to the clinical radiology workflow in order to capture referrer-based and radiologist-based feedback on radiology reporting. We also share our insight into the principles of design thinking as applied in its planning and execution.

Technical Challenges in the Clinical Application of Radiomics

Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.

Clinical Context Generation for Imaging: A Design Thinking-based Analysis of a Pilot Project

Design Thinking is a method for the practical, creative resolution of problems using the strategies used during the process of designing. It is increasingly being used in Medical enterprise to develop a solution-based approach to identify ambiguous problems and create alternative paths to the solution. We faced several challenges in the development of a clinical context generation tool and in this article, we retrospectively assess the usefulness of a Design Thinking approach had it been applied to a project related to Medical Imaging-related clinical context generation.