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Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging., Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed. The new sequences augmenting the clinical protocol were (I) pre-contrast coronal high-resolution T2-weighted single-shot fast spin echo (COR hr T2w SSH FSE) of abdomen and pelvis; and (II) post-contrast coronal high-resolution three-dimensional (3D) T1-weighted modified Dixon (COR hr T1w mDIXON) of abdomen, acquired at five delay times, up to 20 min after administration of a double dose of contrast agent. Quantitative analysis of contrast enhancement was performed using linear regression applied to normalized signal in lesion regions of interest (ROIs). Qualitative analysis was performed by three blinded radiologists., Results: MRI exams from 14 participants (age: mean ± standard deviation, 60±12 years; 71% male) were analyzed. The rate of lesion contrast enhancement was strongly correlated with tumor grade (cumulative nuclear score) (r=-0.65, P<0.02), with 'early' delayed phase (12 min post-contrast) and 'late' delayed phase (19 min post-contrast) performing better for higher grade and lower grade tumors, respectively, in agreement with qualitative scoring of image contrast., Conclusions: High-resolution, high-contrast MRI with extended post-contrast imaging is a viable modality for imaging peritoneal mesothelioma. Multiple, extended (up to 20 min post-contrast) delayed phases are necessary for optimal imaging of peritoneal mesothelioma, depending on the grade of disease., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-13/coif). H.L.K. has received consulting fees from AstraZeneca and is on the advisory board of Tempus, Bluestar Genomics, and Sanofi. R.M.E. receives royalties and licensing fees for computer-aided diagnosis technologies through the University of Chicago. A.O. has received funding from NIH and the Sanford J. Grossman Charitable Trust, has received payment for expert testimony, and is a co-owner of Quantitative MR Imaging Solutions (QMIS). S.G.A. is a past president of the International Mesothelioma Interest Group. K.K.T. has received consulting fees from Merck for unrelated work. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.

Rationale and Objectives: Managing and supervising the complex imaging examinations performed for clinical research in an academic medical center can be a daunting task. Coordinating with both radiology and research staff to ensure that the necessary imaging is performed, analyzed, and delivered in accordance with the research protocol is nontrivial. The purpose of this communication is to report on the establishment of a new Human Imaging Research Office (HIRO) at our institution that provides a dedicated infrastructure to assist with these issues and improve collaborations between radiology and research staff., Materials and Methods: The HIRO was created with three primary responsibilities: 1) coordinate the acquisition of images for clinical research per the study protocol, 2) facilitate reliable and consistent assessment of disease response for clinical research, and 3) manage and distribute clinical research images in a compliant manner., Results: The HIRO currently provides assistance for 191 clinical research studies from 14 sections and departments within our medical center and performs quality assessment of image-based measurements for six clinical research studies. The HIRO has fulfilled 1806 requests for medical images, delivering 81,712 imaging examinations (more than 44.1 million images) and related reports to investigators for research purposes., Conclusions: The ultimate goal of the HIRO is to increase the level of satisfaction and interaction among investigators, research subjects, radiologists, and other imaging professionals. Clinical research studies that use the HIRO benefit from a more efficient and accurate imaging process. The HIRO model could be adopted by other academic medical centers to support their clinical research activities; the details of implementation may differ among institutions, but the need to support imaging in clinical research through a dedicated, centralized initiative should apply to most academic medical centers., (Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.)

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process., Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus., Results: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings., Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Rationale and Objectives: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training, and testing. The integrity of this "truth," however, must be established before investigators commit to this "gold standard" as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the "truth" collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database., Materials and Methods: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the "blinded read phase"), radiologists independently identified and annotated lesions, assigning each to one of three categories: "nodule >or=3 mm," "nodule <3 mm," or "non-nodule >or=3 mm." For the second read (the "unblinded read phase"), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional., Results: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process., Conclusions: The establishment of "truth" must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.

Rationale and Objectives: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers., Materials and Methods: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading., Results: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future., Conclusions: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

Rationale and Objectives: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured., Materials and Methods: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule <3 mm," or "non-nodule >or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus., Results: After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist., Conclusion: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.