Translating New Imaging Technologies to Clinical Practice

Toward Uniform Insurer Coverage for Functional MRI Following Severe Brain Injury

Functional magnetic resonance imaging (fMRI) now promises to improve diagnostic and prognostic accuracy for patients with disorders of consciousness, and accordingly has been endorsed by professional society guidelines, including those of the American Academy of Neurology, American College of Rehabilitation Medicine, National Institute on Disability, Independent Living, and Rehabilitation Research, and the European Academy of Neurology. Despite multiple professional society endorsements of fMRI in evaluating patients with disorders of consciousness following severe brain injury, insurers have yet to issue clear guidance regarding coverage of fMRI for this indication. Lack of insurer coverage may be a rate-limiting barrier to accessing this technique, which could uncover essential diagnostic and prognostic information for patients and their families. The emerging clinical and ethical case for harmonized insurer recognition and reimbursement of fMRI for vulnerable persons following severe brain injury with disorders of consciousness is explained and critically evaluated.

Radiology, Mobile Devices, and Internet of Things (IoT)

Radiology by its nature is intricately connected to the Internet and is at the forefront of technology in medicine. The past few years have seen a dramatic rise in Internet-based technology in healthcare, with imaging as a core application. Numerous Internet-based applications and technologies have made forays into medicine, and for radiology it is more seamless than in other clinical specialties. Many applications in the practice of radiology are Internet based and more applications are being added every day. Introduction of mobile devices and their integration into imaging workflow has reinforced the role played by the Internet in radiology. Due to the rapid proliferation of wearable devices and smartphones, IoT-enabled technology is evolving healthcare from conventional hub-based systems to more personalized healthcare systems. This article briefly discusses how the IoT plays a useful role in daily imaging workflow and current and potential future applications, how mobile devices can be integrated into radiology workflows, and the impact of the IoT on resident and medical student education, research, and patient engagement in radiology.

Digital Breast Tomosynthesis: Radiologist Learning Curve

Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density. © RSNA, 2019 See also the editorial by Hooley in this issue.

Radiologist Engagement as a Potential Barrier to the Clinical Translation of Quantitative Imaging for the Assessment of Tumor Heterogeneity

RATIONALE AND OBJECTIVE

This study aims to identify potential barriers to the clinical implementation of quantitative imaging for the assessment of tumor heterogeneity.

MATERIALS AND METHODS

An 18-month prospective observational study was undertaken in which the clinical implementation of computed tomography texture analysis (CTTA) as a technique for quantifying tumor heterogeneity in patients with non-small cell lung cancer was assessed using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

RESULTS

Adopters of the technology comprised five specialists with dual accreditation in radiology and nuclear medicine supervising two trainees. Tumor heterogeneity information was extracted and reported in 190 of 322 eligible cases (59%) and presented at the multidisciplinary team meeting in 124 of 152 patients (82%) for whom CTTA had been performed. The maximum proportion of eligible cases in which heterogeneity information had been extracted and reported in any quarter was 80%, but fell in the latter half of the study. The maximum frequency with which available CTTA results were presented at the multidisciplinary team meeting in any quarter was 92% and was maintained in the latter part of the study. Significant differences in survival were observed for patients categorized using the two reported CTTA values (P = 0.004 and P = 0.0057, respectively).

CONCLUSIONS

Radiologist engagement is a potential barrier to the effective translation of quantitative imaging assessments of tumor heterogeneity into clinical practice and will need to be addressed before tumor heterogeneity information can successfully contribute to clinical decision making in oncology.