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Purpose:To demonstrate possible superiority in the performance of a radiologist who is tasked with detecting actionable nodules and aided by the bone suppression and soft-tissue visualization algorithm of a new software program that produces a modifi ed image by suppressing the ribs and clavicles, fi ltering noise, and equalizing the contrast in the area of the lungs. Materials and Methods:The study and use of anonymized and deidentifi ed data received approval from the MedStar-Georgetown University Oncology Institutional Review Board. Informed consent was obtained from 15 study radiologists. The study radiologists participated as observers in a reader study of 368 patients in an approximately 2:1 cancer-free-to-cancer ratio. The localized receiver operating characteristic (LROC) method was used for analyses. Images were rerandomized for each radiologist. Each patient image was sequentially read, fi rst with the standard radiograph and then with the software-aided image. Normal studies were confi rmed with computed tomography (CT), follow-up, and/or panel consensus. Results:Each reader and the combined scores of the 15 readers showed improvement. The area under the combined LROC curve increased signifi cantly from 0.460 unaided to 0.558 aided by visualization software ( P = .0001). When measured according to the reader's indication that a case should be sent or not sent for CT or biopsy, sensitivity for cancer detection increased from 49.5% unaided to 66.3% aided by software ( P , .0001); specifi city decreased from 96.1% to 91.8% ( P = .004). Seventy-four percent of the aided detections occurred in cancers with 70% or greater overlap of the bone and the nodule. Conclusion:The radiologists using visualization software signifi cantly increased their detection of lung cancers and benign nodules.Clinical trial registration no. NCT00906789q RSNA, 2011Supplemental material: http://radiology.rsna.org/lookup /suppl

The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.

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Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.