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

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)

Computed tomography-based anatomic assessment overestimates local tumor recurrence in patients with mass-like consolidation after stereotactic body radiotherapy for early-stage non-small cell lung cancer.

PURPOSE: To investigate pulmonary radiologic changes after lung stereotactic body radiotherapy (SBRT), to distinguish between mass-like fibrosis and tumor recurrence. METHODS AND MATERIALS: Eighty consecutive patients treated with 3- to 5-fraction SBRT for early-stage peripheral non-small cell lung cancer with a minimum follow-up of 12 months were reviewed. The mean biologic equivalent dose received was 150 Gy (range, 78-180 Gy). Patients were followed with serial CT imaging every 3 months. The CT appearance of consolidation was defined as diffuse or mass-like. Progressive disease on CT was defined according to Response Evaluation Criteria in Solid Tumors 1.1. Positron emission tomography (PET) CT was used as an adjunct test. Tumor recurrence was defined as a standardized uptake value equal to or greater than the pretreatment value. Biopsy was used to further assess consolidation in select patients. RESULTS: Median follow-up was 24 months (range, 12.0-36.0 months). Abnormal mass-like consolidation was identified in 44 patients (55%), whereas diffuse consolidation was identified in 12 patients (15%), at a median time from end of treatment of 10.3 months and 11.5 months, respectively. Tumor recurrence was found in 35 of 44 patients with mass-like consolidation using CT alone. Combined with PET, 10 of the 44 patients had tumor recurrence. Tumor size (hazard ratio 1.12, P=.05) and time to consolidation (hazard ratio 0.622, P=.03) were predictors for tumor recurrence. Three consecutive increases in volume and increasing volume at 12 months after treatment in mass-like consolidation were highly specific for tumor recurrence (100% and 80%, respectively). Patients with diffuse consolidation were more likely to develop grade ≥ 2 pneumonitis (odds ratio 26.5, P=.02) than those with mass-like consolidation (odds ratio 0.42, P=.07). CONCLUSION: Incorporating the kinetics of mass-like consolidation and PET to the current criteria for evaluating posttreatment response will increase the likelihood of correctly identifying patients with progressive disease after lung SBRT

A Call for the Structured Physicist Report

Introduction: The field of diagnostic radiology continues to struggle with the clinical adoption of the structured interpretive report, with many radiologists preferring a semistructured, free-text dictation style to a more rigid, highly structured approach that some professional leaders have promoted [1]. Although structured reporting compliance in the radiologist community has been difficult to achieve, diagnostic radiologists have been thinking about and discussing this important issue for many years; it is also a part of the ACR’s Imaging 3.0_ campaign [2]. In the breast imaging community, the well-established BI-RADS_ recommendations produce a very structured report, with a discussion of interpretive findings culminating in a numeric BI-RADS score ranging from 0 to 6 [3]. Unlike some interpretive radiology reports, which can be ambiguous in terms of the next course of action, the BI-RADS scale is not only a diagnostic scale but also prescriptive of what the necessary follow-up should be