Cardiac Incidental Findings on Abdominopelvic Computed Tomography: Prevalence and Association with Subsequent Cardiovascular Events.

PURPOSE: The aim of this study was to assess the prevalence of reportable cardiac findings detected on abdominopelvic CTs and the association with subsequent cardiovascular events. MATERIALS AND METHODS: We performed a retrospective search of electronic medical record of patients who underwent abdominopelvic CT between November 2006 and November 2011 with a clinical history of upper abdominal pain. A radiologist blinded to the original CT report reviewed all 222 cases for the presence of pertinent reportable cardiac findings. The original CT report was also evaluated for documentation of pertinent reportable cardiac findings. The following findings were recorded on all CTs: presence of coronary calcification, fatty metaplasia, ventricle wall thinning and thickening, valve calcification or prosthesis, heart/chamber enlargement, aneurysm, mass, thrombus, device, air within ventricles, abnormal pericardium, prior sternotomy, and adhesions if prior sternotomy. Medical records were reviewed to identify cardiovascular events on follow-up in patients with the presence or absence of cardiac findings. We compared the distribution findings in patients with and without cardiac events using the Wilcoxon test (for continuous variables) and the Pearson's chi-squared test (for categorical variables). RESULTS: Eighty-five of 222 (38.3%) patients (52.7% females, median age 52.5 years) had at least one pertinent reportable cardiac finding on the abdominopelvic CT, with a total of 140 findings in this group. From the total 140 findings, 100 (71.4%) were not reported. The most common findings seen on abdominal CTs were: coronary artery calcification (66 patients), heart or chamber enlargement (25), valve abnormality (19), sternotomy and surgery signs (9), LV wall thickening (7), device (5), LV wall thinning (2), pericardial effusion (5), and others (3). After a mean follow-up of 43.9 months, 19 cardiovascular events were found in the cohort (transient ischemic attack, cerebrovascular accident, myocardial infarction, cardiac arrest, acute arrhythmia, palpitation, syncope and acute chest pain). Only 1 event occurred in the group of patients with no incidental pertinent reportable cardiac findings (1/137 = 0.73%). All other 18 events occurred in patients with incidental pertinent reportable cardiac findings (18/85 = 21.2%), which was significantly different (p < 0.0001). One out of the total 19 events in the overall group (5.24%) occurred in a patient with no incidental pertinent reportable cardiac findings while 18 of 19 total events (94.74%) occurred with patients with incidental pertinent reportable cardiac findings, which was also significantly different (p < 0.001). Fifteen of the total events (79%) occurred in patients in whom the incidental pertinent reportable cardiac findings were not reported, which was significantly different (p < 0.001) from the four events that occurred in patients in whom the incidental pertinent reportable cardiac findings were reported or had no findings. CONCLUSIONS: Incidental pertinent reportable cardiac findings are common on abdominal CTs and are frequently not reported by radiologists. These findings are of clinical relevance since patients with pertinent reportable cardiac findings have a significantly higher incidence of cardiovascular events on follow-up.

Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs.

OBJECTIVE: To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains. METHODS: We evaluated a publicly available DCNN-based bone age application. The DCNN was trained on 12,612 pediatric hand radiographs and won the 2017 RSNA Pediatric Bone Age Challenge (concordance of 0.991 with radiologist ground-truth). We used the application to analyze 50 left-hand radiographs (appropriate data inputs) and seven classes of inappropriate data inputs in radiological (i.e., chest radiographs) and non-radiological (i.e., image of street numbers) domains. For each image, we noted if (1) the application distinguished between appropriate and inappropriate data inputs and (2) inference time per image. Mean inference times were compared using ANOVA. RESULTS: The 16Bit Bone Age application calculated bone age for all pediatric hand radiographs with mean inference time of 1.1 s. The application did not distinguish between pediatric hand radiographs and inappropriate image types, including radiological and non-radiological domains. The application inappropriately calculated bone age for all inappropriate image types, with mean inference time of 1.1 s for all categories (p = 1). CONCLUSION: A publicly available DCNN-based bone age application failed to distinguish between appropriate and inappropriate data inputs and calculated bone age for inappropriate images. The awareness of inappropriate outputs based on inappropriate DCNN input is important if tasks such as bone age determination are automated, emphasizing the need for appropriate oversight at the data input and verification stage to avoid unrecognized erroneous results.

Clinics in diagnostic imaging (191). Multiple system atrophy-cerebellar type (MSA-C).

A 49-year-old Chinese man was evaluated for progressive uncoordinated movements, dysphagia and urinary symptoms. Magnetic resonance imaging demonstrated a cruciform pattern of T2-weighted hyperintensity within the pons and selective atrophy of the cerebellar hemispheres and pons. The clinical history and radiological findings were consistent with a diagnosis of multiple system atrophy-cerebellar type. This article discussed the background, proposed mechanisms, diagnosis, radiological characteristics, prognosis and management of multiple system atrophy-cerebellar type.

Clinics in diagnostic imaging (190). Subacute combined degeneration of the spinal cord (SCD).

A 54-year-old man presented with progressive onset of lower limb paraesthesias, sensory ataxia, gait instability and lower limb weakness. Laboratory findings revealed low serum B12 levels. Magnetic resonance imaging showed long-segment symmetrically increased T2 signal within the dorsal columns of the spinal cord in the lower thoracic spine. The conglomeration of findings was consistent with a diagnosis of subacute combined degeneration of the spinal cord (SCD). Aside from mild residual paraesthesias, the patient's symptoms largely resolved after treatment with intramuscular injections of vitamin B12. The clinical presentation, pathophysiology, clinical and radiologic differential diagnosis, and management of SCD were described.

Clinics in diagnostic imaging (188). Transient global amnesia (TGA).

A 60-year-old woman was travelling on a plane with her spouse when she suddenly developed acute-onset disorientation to time and space. According to her husband, she repetitively questioned her whereabouts and complained of a minor headache. Upon landing, she was immediately brought to the emergency room and subsequent magnetic resonance imaging was performed, which showed multiple punctate 2-3 mm regions of diffusion-weighted imaging hyperintensity in the medial aspects of both temporal lobes. The conglomeration of clinical history and radiological findings was most suggestive of transient global amnesia. This article discussed the background, proposed mechanisms, diagnosis, radiological characteristics and management of transient global amnesia.

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Implementation of Digital Awareness Strategies to Engage Patients and Providers in a Lung Cancer Screening Program: Retrospective Study.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths in the United States. Despite mandated insurance coverage for eligible patients, lung cancer screening rates remain low. Digital platforms, including social media, provide a potentially valuable tool to enhance health promotion and patient engagement related to lung cancer screening (LCS). OBJECTIVE: The aim was to assess the effectiveness of LCS digital awareness campaigns on utilization of low-dose computed tomography (LDCT) and visits to institutional online educational content. METHODS: A pay-per-click campaign utilizing Google and Facebook targeted adults aged 55 years and older and caregivers aged 18 years and older (eg, spouses, adult children) with LCS content during a 20-week intervention period from May to September 2016. A concurrent pay-per-click campaign using LinkedIn and Twitter targeted health care providers with LCS content. Geographic target radius was within 60 miles of an academic medical center. Social media data included aggregate demographics and click-through rates (CTRs). Primary outcome measures were visits to institutional Web pages and scheduled LDCT exams. Study period was 20 weeks before, during, and after the digital awareness campaigns. RESULTS: Weekly visits to the institutional LCS Web pages were significantly higher during the digital awareness campaigns compared to the 20-week period prior to implementation (mean 823.9, SD 905.8 vs mean 51, SD 22.3, P=.001). The patient digital awareness campaign surpassed industry standard CTRs on Google (5.85%, 1108/18,955 vs 1.8%) and Facebook (2.59%, 47,750/1,846,070 vs 0.8%). The provider digital awareness campaign surpassed industry standard CTR on LinkedIn (1.1%, 630/57,079 vs 0.3%) but not Twitter (0.19%, 1139/587,133 vs 0.25%). Mean scheduled LDCT exam volumes per week before, during, and after the digital awareness campaigns were 17.4 (SD 7.5), 20.4 (SD 5.4), and 26.2 (SD 6.4), respectively, with the difference between the mean number of scheduled exams after the digital awareness campaigns and the number of exams scheduled before and after the digital awareness campaigns being statistically significant (P<.001). CONCLUSIONS: Implementation of the LCS digital awareness campaigns was associated with increased visits to institutional educational Web pages and scheduled LDCT exams. Digital platforms are an important tool to enhance health promotion activities and engagement with patients and providers.

Penis Transplantation: First US Experience.

OBJECTIVE: We describe the first successful penis transplant in the United States in a patient with a history of subtotal penectomy for penile cancer. BACKGROUND: Penis transplantation represents a new paradigm in restoring anatomic appearance, urine conduit, and sexual function after genitourinary tissue loss. To date, only 2 penis transplants have been performed worldwide. METHODS: After institutional review board approval, extensive medical, surgical, and radiological evaluations of the patient were performed. His candidacy was reviewed by a multidisciplinary team of surgeons, physicians, psychiatrists, social workers, and nurse coordinators. After appropriate donor identification and recipient induction with antithymocyte globulin, allograft procurement and recipient preparation took place concurrently. Anastomoses of the urethra, corpora, cavernosal and dorsal arteries, dorsal vein, and dorsal nerves were performed, and also inclusion of a donor skin pedicle as the composite allograft. Maintenance immunosuppression consisted of mycophenolate mofetil, tacrolimus, and methylprednisolone. RESULTS: Intraoperative, the allograft had excellent capillary refill and strong Doppler signals after revascularization. Operative reinterventions on postoperative days (PODs) 2 and 13 were required for hematoma evacuation and skin eschar debridement. At 3 weeks, no anastomotic leaks were detected on urethrogram, and the catheter was removed. Steroid resistant-rejection developed on POD 28 (Banff I), progressed by POD 32 (Banff III), and required a repeat course of methylprednisolone and antithymocyte globulin. At 7 months, the patient has recovered partial sensation of the penile shaft and has spontaneous penile tumescence. Our patient reports increased overall health satisfaction, dramatic improvement of self-image, and optimism for the future. CONCLUSIONS: We have shown that it is feasible to perform penile transplantation with excellent results. Furthermore, this experience demonstrates that penile transplantation can be successfully performed with conventional immunosuppression. We propose that our successful penile transplantation pilot experience represents a proof of concept for an evolution in reconstructive transplantation.

Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health.

BACKGROUND: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. OBJECTIVE: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient's electronic health record (EHR). METHODS: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients' EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. RESULTS: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. CONCLUSIONS: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program.

JOURNAL CLUB: Structured Feedback From Patients on Actual Radiology Reports: A Novel Approach to Improve Reporting Practices.

OBJECTIVE: Patient perceptions of radiology reports are largely unknown. The objective of the present study is to describe our experience receiving structured feedback from patients on actual radiology reports as a means of improving reporting practices. MATERIALS AND METHODS: Eight reports (two for radiographs, two for ultrasound images, two for CT scans, and two for MR images) were randomly selected from our system for review. For each report, patients were asked to rate their level of comprehension, identify any problems in the report, and, in the free-text portion of the feedback form, indicate any questions about the report that they may have. Potentially confounding factors were also examined. RESULTS: A total of 104 patients (46 men and 58 women) participated in the study (for a total of 832 evaluations). The median score for report comprehension was 2.5 (on a scale of 1-5), with the most common problems affecting comprehension identified as "unclear or technical language" (mentioned in 59.6% of evaluations) and the report being "too long" (mentioned in 10.2% of evaluations). A request for an explanation of the report in lay terms (noted in 20.1% of evaluations) was the most common request mentioned in the free-text portion of the feedback form. An inverse relationship existed between report length and patient comprehension (p < 0.001). Patients who had prior experience with their own radiology reports indicated having greater comprehension than did patients with no prior experience (p = 0.003). No correlation between the educational status and report comprehension of the patients was identified (p = 0.488). CONCLUSION: Radiology reports are not well understood by patients, who identify technical language and the long length of reports as the most common problems affecting their comprehension. Longer reports tend to be less well understood.

Fully Automated Deep Learning System for Bone Age Assessment.

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.

A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

BACKGROUND: With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records and administrative databases. Three areas that may have an impact on readmission but are poorly captured using structured data sources are patients' physical function, cognitive status, and psychosocial environment and support. OBJECTIVE OF THE STUDY: The objective of the study was to build a discriminative model using information germane to these 3 areas to identify hospitalized patients' risk for 30-day all cause readmissions. RESEARCH DESIGN: We conducted clinician focus groups to identify language used in the clinical record regarding these 3 areas. We then created a dataset including 30,000 inpatients, 10,000 from each of 3 hospitals, and searched those records for the focus group-derived language using natural language processing. A 30-day readmission prediction model was developed on 75% of the dataset and validated on the other 25% and also on hospital specific subsets. RESULTS: Focus group language was aggregated into 35 variables. The final model had 16 variables, a validated C-statistic of 0.74, and was well calibrated. Subset validation of the model by hospital yielded C-statistics of 0.70-0.75. CONCLUSIONS: Deriving a 30-day readmission risk prediction model through identification of physical, cognitive, and psychosocial issues using natural language processing yielded a model that performs similarly to the better performing models previously published with the added advantage of being based on clinically relevant factors and also automated and scalable. Because of the clinical relevance of the variables in the model, future research may be able to test if targeting interventions to identified risks results in reductions in readmissions.

Differing Interpretations of Report Terminology Between Primary Care Physicians and Radiologists.

PURPOSE: The lexicons of the radiologist and the referring physician may not be synonymous, which could cause confusion with radiology reporting. To further explore this possibility, we surveyed radiologists and primary care physicians (PCPs) regarding their respective interpretations of report terminology. METHODS: A survey was distributed to radiologists and PCPs through an internal listserv. Respondents were asked to provide an interpretation of the statistical likelihood of the presence of metastatic disease based upon the terminology used within a hypothetical radiology report. Ten common modifying terms were evaluated. Potential responses for the statistical likelihoods included 0%-25%, 26%-50%, 51%-75%, 76%-99%, and 100%. Differences between the groups were evaluated using either a χ2 test or Fisher exact test, as appropriate. RESULTS: The phrases "diagnostic for metastatic disease" and "represents metastatic disease" were selected by a high percentage of both groups as conferring a 100% likelihood of "true metastatic disease." The phrases "cannot exclude metastatic disease" and "may represent metastatic disease" were selected by a high proportion of both groups as conferring a 0% likelihood of "true metastatic disease." Radiologists assigned a higher statistical likelihood to the terms "diagnostic for metastatic disease" (P = .016), "represents metastatic disease" (P = .004), "suspicious for metastatic disease" (P = .04), "consistent with metastatic disease" (P < .0001), and "compatible with metastatic disease" (P = .003). CONCLUSION: A qualitative agreement among radiologists and PCPs exists concerning the significance of the evaluated terminology, although radiologists assigned a higher statistical likelihood than PCPs for several phrases.

Radiology Malpractice Claims in the United States From 2008 to 2012: Characteristics and Implications.

PURPOSE: The aim of this study was to compare the frequency and liability costs associated with radiology malpractice claims relative to other medical services and to evaluate the clinical context and case disposition associated with radiology malpractice claims. METHODS: This HIPAA-compliant study was exempted from institutional review board approval. The Comparative Benchmarking System database, a repository of more than 300,000 medical malpractice cases in the United States, was queried for closed claims over a five-year period (2008-2012). Claims were categorized by the medical service primarily responsible for the claim and the paid total loss. For all cases in which radiology was the primary responsible service, the case abstracts were evaluated to determine injury severity, claimant type by setting, claim allegation, process of care involved, case disposition, modality involved, and body section. Intracategory comparisons were made on the basis of the frequency of indemnity payment and total indemnity payment for paid cases, using χ(2) and Wilcoxon rank-sum tests. RESULTS: Radiology was the eighth most likely responsible service to be implicated in a medical malpractice claim, with a median total paid loss (indemnity payment plus defense cost plus administrative expense) per closed case of $30,091 (mean, $205,619 ± $508,883). Radiology claims were most commonly associated with high- and medium-severity injuries (93.3% [820 of 879]; 95% confidence interval [CI], 91.7%-94.95%), the outpatient setting (66.3% [581 of 876]; 95% CI, 63.0%-69.2%), and diagnosis-related allegations (ie, failure to diagnose or delayed diagnosis) (57.3% [504 of 879]; 95% CI, 54.0%-60.6%). A high proportion of claims pertained to cancer diagnoses (44.0% [222 of 504]; 95% CI, 39.7%-48.3%). A total of 62.3% (548 of 879; 95% CI, 59.1%-65.5%) of radiology claims were closed without indemnity payments; 37.7% (331 of 879; 95% CI, 34.5%-40.9%) were closed with a median indemnity payment of $175,000 (range, $112-$6,691,762; mean $481,094 ± $727,636). CONCLUSIONS: Radiology malpractice claims most commonly involve diagnosis-related allegations in the outpatient setting, particularly cancer diagnoses, with approximately one-third of claims resulting in payouts to the claimants.

Effectiveness of second-opinion radiology consultations to reassess the cervical spine CT scans: a study on trauma patients referred to a tertiary-care hospital.

PURPOSE: A second opinion is a valuable resource in confirming proper medical diagnosis and treatment. This study evaluates the effectiveness of second-opinion radiology consultations to reassess the cervical spine computed tomography (CT) scans of the trauma patients referred to our hospital. METHODS: Cervical spine CT scans of 301 consecutive adult trauma patients, who were referred to our hospital from outside institutions, were analyzed. The emergency radiologists at our institution completed the over-read reports on the CT images obtained at the outside facilities. A single radiologist compared the outside- and over-read reports and determined the discrepancy of the radiologic reports. RESULTS: Based on the outside reports, 31% of the CT scans had cervical traumatic injury. In 92% of patients, the first-read and the over-read reports had consistent radiologic findings. About 90% of the positive, and 93% of the negative radiologic findings, were reported consistently in the over-read reports. Our analysis showed that the over-read reporting resulted in reassurance of negative findings in 63%; confirmation of positive findings in 29%; clearing a false diagnosis in 3%; and detection of a missed diagnosis in 5%. A rescan was done in 80% of patients with inconsistent and 20% of patients with consistent findings (P < 0.05). The most common missed radiologic findings in the first-reports were transverse and spinous process fractures and the most common misdiagnoses were dens fractures. CONCLUSION: For a service offering second-opinion consultations on cervical spine trauma, review of outside CT studies improves diagnosis and benefits patient care.

Creating a Patient-Centered Radiology Practice Through the Establishment of a Diagnostic Radiology Consultation Clinic.

OBJECTIVE: The purposes of this study were to assess the feasibility of and to create a referral mechanism for a diagnostic radiology consultation clinic. SUBJECTS AND METHODS: A pilot program was instituted with patients from a single primary care clinic over a 3-week period. Patients with findings of common problems at routine imaging, such as atherosclerosis, emphysema, and hepatic steatosis, were eligible to participate. As the patients arrived for their routine primary care visits, office staff informed them of the opportunity to formally meet with a radiologist to review their most recent imaging findings. The office staff of the primary care clinic then contacted the radiologist covering the diagnostic radiology consultation clinic to schedule a consultation. A survey was administered before and after the session. RESULTS: Twenty-two patients participated (88% participation rate). Participants rated the consultation as very helpful (mean, 4.8 on 1-5 scale), and all participants would take the opportunity to review studies with the radiologist again. Significantly more patients preferred the involvement of the radiologist in communicating the results of an imaging examination after the consultation compared with before the consultation (p = 0.001). After the consultation session, patients had significantly improved understanding of a radiologist's role (p = 0.004), and all participants were able to correctly identify the radiologist as a physician who interprets medical images. CONCLUSION: A referral mechanism for a diagnostic radiology consultation clinic can be effectively integrated into the everyday workflow of both the referring physician and the radiologist. The consultations are useful to patients and help to increase their awareness of the role of the radiologist.

Assessment of Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction for Reduced Dose Abdominal Computed Tomography.

PURPOSE: To compare standard of care and reduced dose (RD) abdominal computed tomography (CT) images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), model-based iterative reconstruction (MBIR) techniques. MATERIALS AND METHODS: In an Institutional Review Board-approved, prospective clinical study, 28 patients (mean age 59 ± 13 years ), undergoing clinically indicated routine abdominal CT on a 64-channel multi-detector CT scanner, gave written informed consent for acquisition of an additional RD (<1 milli-Sievert) abdomen CT series. Sinogram data of RD series were reconstructed with FBP, ASIR, and MBIR and compared with FBP images of standard dose abdomen CT. Two radiologists performed randomized, independent, and blinded comparison for lesion detection, lesion margin, visibility of normal structures, and diagnostic confidence. RESULTS: Mean CT dose index volume was 10 ± 3.4 mGy and 1.3 ± 0.3 mGy for standard and RD CT, respectively. There were 73 "true positive" lesions detected on standard of care CT. Nine lesions (<8 mm in size) were missed on RD abdominal CT images which included liver lesions, liver cysts, kidney cysts, and paracolonic abscess. These lesions were missed regardless of patient size and types of iterative reconstruction techniques used for reconstruction of RD data sets. The visibility of lesion margin was suboptimal in (23/28) patients with RD FBP, (15/28) patients with RD ASIR, and (14/28) patients with RD MBIR compared to standard of care FBP images (P < 0.001). Diagnostic confidence for the assessment of lesions on RD images was suboptimal in most patients regardless of iterative reconstruction techniques. CONCLUSIONS: Clinically significant lesions (< 8 mm) can be missed on abdominal CT examinations acquired at a CT dose index volume of 1.3 mGy regardless of patients' size and reconstruction techniques (FBP, ASIR, and MBIR).