Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (n = 589) and internal test cohort (n = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, P < .001; C index = 0.689, P < .001) and performing comparably with senior radiologists (AUC = 0.79, P = .21; C index = 0.788, P = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, P < .001; C index = 0.798, P < .001) and senior radiologists (AUC = 0.88, P < .001; C index = 0.869, P < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. Keywords: MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. Published under a CC BY 4.0 license.

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events. METHODS: Fully automated AI body composition tools quantifying abdominal aortic calcium, abdominal fat (visceral [VAT], visceral-to-subcutaneous ratio [VSR]), and muscle attenuation (muscle HU) were applied to non-contrast CT examinations in adults undergoing screening CT colonography (CTC). Patients were partitioned into 5 socioeconomic groups based on the national ADI rank at the census block group level. Pearson correlation analysis was performed to determine the association between national ADI and body composition measures. One-way analysis of variance was used to compare means across groups. Odds ratios (ORs) were generated using high-risk, high specificity (90% specificity) body composition thresholds with the most disadvantaged groups being compared to the least disadvantaged group (ADI < 20). RESULTS: 7785 asymptomatic adults (mean age, 57 years; 4361:3424 F:M) underwent screening CTC from April 2004-December 2016. ADI rank data were available in 7644 patients. Median ADI was 31 (IQR 22-43). Aortic calcium, VAT, and VSR had positive correlation with ADI and muscle attenuation had a negative correlation with ADI (all p < .001). Compared with the least disadvantaged group, mean differences for the most disadvantaged group (ADI > 80) were: Aortic calcium (Agatston) = 567, VAT = 27 cm2, VSR = 0.1, and muscle HU = -6 HU (all p < .05). Compared with the least disadvantaged group, the most disadvantaged group had significantly higher odds of having high-risk body composition measures: Aortic calcium OR = 3.8, VAT OR = 2.5, VSR OR = 2.0, and muscle HU OR = 3.1(all p < .001). CONCLUSION: Fully automated CT body composition tools show that socioeconomic disadvantage is associated with high-risk body composition measures and can be used to identify individuals at increased risk for all-cause mortality and adverse cardiovascular events.

CT Colonography Reporting and Data System (C-RADS): Version 2023 Update.

The CT Colonography Reporting and Data System (C-RADS) has withstood the test of time and proven to be a robust classification scheme for CT colonography (CTC) findings. C-RADS version 2023 represents an update on the scheme used for colorectal and extracolonic findings at CTC. The update provides useful insights gained since the implementation of the original system in 2005. Increased experience has demonstrated confusion on how to classify the mass-like appearance of the colon consisting of soft tissue attenuation that occurs in segments with acute or chronic diverticulitis. Therefore, the update introduces a new subcategory, C2b, specifically for mass-like diverticular strictures, which are likely benign. Additionally, the update simplifies extracolonic classification by combining E1 and E2 categories into an updated extracolonic category of E1/E2 since, irrespective of whether a finding is considered a normal variant (category E1) or an otherwise clinically unimportant finding (category E2), no additional follow-up is required. This simplifies and streamlines the classification into one category, which results in the same management recommendation.

Natural History of Colorectal Polyps Undergoing Longitudinal in Vivo CT Colonography Surveillance.

Background The natural history of colorectal polyps is not well characterized due to clinical standards of care and other practical constraints limiting in vivo longitudinal surveillance. Established CT colonography (CTC) clinical screening protocols allow surveillance of small (6-9 mm) polyps. Purpose To assess the natural history of colorectal polyps followed with CTC in a clinical screening program, with histopathologic correlation for resected polyps. Materials and Methods In this retrospective study, CTC was used to longitudinally monitor small colorectal polyps in asymptomatic adult patients from April 1, 2004, to August 31, 2020. All patients underwent at least two CTC examinations. Polyp growth patterns across multiple time points were analyzed, with histopathologic context for resected polyps. Regression analysis was performed to evaluate predictors of advanced histopathology. Results In this study of 475 asymptomatic adult patients (mean age, 56.9 years ± 6.7 [SD]; 263 men), 639 unique polyps (mean initial diameter, 6.3 mm; volume, 50.2 mm3) were followed for a mean of 5.1 years ± 2.9. Of these 639 polyps, 398 (62.3%) underwent resection and histopathologic evaluation, and 41 (6.4%) proved to be histopathologically advanced (adenocarcinoma, high-grade dysplasia, or villous content), including two cancers and 38 tubulovillous adenomas. Advanced polyps showed mean volume growth of +178% per year (752% per year for adenocarcinomas) compared with +33% per year for nonadvanced polyps and -3% per year for unresected, unretrieved, or resolved polyps (P < .001). In addition, 90% of histologically advanced polyps achieved a volume of 100 mm3 and/or volume growth rate of 100% per year, compared with 29% of nonadvanced and 16% of unresected or resolved polyps (P < .001). Polyp volume-to-diameter ratio was also significantly greater for advanced polyps. For polyps observed at three or more time points, most advanced polyps demonstrated an initial slower growth interval, followed by a period of more rapid growth. Conclusion Small colorectal polyps ultimately proving to be histopathologically advanced neoplasms demonstrated substantially faster growth and attained greater overall size compared with nonadvanced polyps. Clinical trial registration no. NCT00204867 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Dachman in this issue.

Automated detection of incidental abdominal aortic aneurysms on computed tomography.

PURPOSE: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. MATERIALS AND METHODS: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs-maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. RESULTS: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. CONCLUSION: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. METHODS: In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis. RESULTS: A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. CONCLUSION: Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

CT Navigation for Percutaneous Needle Placement: How I Do It.

CT navigation (CTN) has recently been developed to combine many of the advantages of conventional CT and CT-fluoroscopic guidance for needle placement. CTN systems display real-time needle position superimposed on a CT dataset. This is accomplished by placing electromagnetic (EM) or optical transmitters/sensors on the patient and needle, combined with fiducials placed within the scan field to superimpose a known needle location onto a CT dataset. Advantages of CTN include real-time needle tracking using a contemporaneous CT dataset with the patient in the treatment position, reduced radiation to the physician, facilitation of procedures outside the gantry plane, fewer helical scans during needle placement, and needle guidance based on diagnostic-quality CT datasets. Limitations include the display of a virtual (vs actual) needle position, which can be inaccurate if the needle bends, the fiducial moves, or patient movement occurs between scans, and limitations in anatomical regions with a high degree of motion such as the lung bases. This review summarizes recently introduced CTN technologies in comparison to historical methods of CT needle guidance. A "How I do it" section follows, which describes how CT navigation has been integrated into the study center for both routine and challenging procedures, and includes step-by-step explanations, technical tips, and pitfalls.

Comparison of MiraLAX and magnesium citrate for bowel preparation at CT colonography.

PURPOSE: To compare MiraLAX, a hypo-osmotic lavage, and magnesium citrate (MgC), a hyper-osmotic agent for bowel preparation at CTC. METHODS: 398 total screening CTC studies were included in this retrospective, single institution study. 297 underwent preparation with a double-dose MgC regimen (mean age, 61 ± 5.5 years; 142 male/155 female) and 101 with 8.3 oz (equivalent to 238 g PEG) of MiraLAX (mean age, 60 ± 9.6 years; 45 male/56 female). Oral contrast for tagging purposes was utilized in both regimens. Studies were retrospectively analyzed for residual fluid volume and attenuation by automated analysis, as well for subjective oral contrast coating of the normal colonic wall and polyps. 50 patients underwent successive CTC studies utilizing each agent (mean, 6.1 ± 1.7 years apart), allowing for intra-patient comparison. Chi-squared, Fisher's exact, McNemar, and t-tests were used for data comparison. RESULTS: Residual fluid volume (as percentage of total colonic volume) and fluid density was 7.2 ± 4.2% and 713 ± 183 HU for the MgC cohort and 8.7 ± 3.8% and 1044 HU ± 274 for the MiraLAX cohort, respectively (p = 0.001 and p < 0.001, respectively). Similar results were observed for the intra-patient cohort. Colonic wall coating negatively influencing interpretation was noted in 1.7% of MgC vs. 6.9% of MiraLAX examinations (p = 0.008). Polyps were detected in 12% of all MgC vs. 16% of all MiraLAX CTCs (p = 0.29). CONCLUSION: CTC bowel preparation with the hypo-osmotic MiraLAX agent appears to provide acceptable diagnostic quality that is comparable to the hyper-osmotic MgC agent, especially when factoring in patient safety and tolerance.

Growth rates and histopathological outcomes of small (6-9 mm) colorectal polyps based on CT colonography surveillance and endoscopic removal.

BACKGROUND AND AIMS: The natural history of small polyps is not well established and rests on limited evidence from barium enema studies decades ago. Patients with one or two small polyps (6-9 mm) at screening CT colonography (CTC) are offered CTC surveillance at 3 years but may elect immediate colonoscopy. This practice allows direct observation of the growth of subcentimetre polyps, with histopathological correlation in patients undergoing subsequent polypectomy. DESIGN: Of 11 165 asymptomatic patients screened by CTC over a period of 16.4 years, 1067 had one or two 6-9 mm polyps detected (with no polyps ≥10 mm). Of these, 314 (mean age, 57.4 years; M:F, 141:173; 375 total polyps) elected immediate colonoscopic polypectomy, and 382 (mean age 57.0 years; M:F, 217:165; 481 total polyps) elected CTC surveillance over a mean of 4.7 years. Volumetric polyp growth was analysed, with histopathological correlation for resected polyps. Polyp growth and regression were defined as volume change of ±20% per year, with rapid growth defined as +100% per year (annual volume doubling). Regression analysis was performed to evaluate predictors of advanced histology, defined as the presence of cancer, high-grade dysplasia (HGD) or villous components. RESULTS: Of the 314 patients who underwent immediate polypectomy, 67.8% (213/314) harboured adenomas, 2.2% (7/314) with advanced histology; no polyps contained cancer or HGD. Of 382 patients who underwent CTC surveillance, 24.9% (95/382) had polyps that grew, while 62.0% (237/382) remained stable and 13.1% (50/382) regressed in size. Of the 58.6% (224/382) CTC surveillance patients who ultimately underwent colonoscopic resection, 87.1% (195/224) harboured adenomas, 12.9% (29/224) with advanced histology. Of CTC surveillance patients with growing polyps who underwent resection, 23.2% (19/82) harboured advanced histology vs 7.0% (10/142) with stable or regressing polyps (OR: 4.0; p<0.001), with even greater risk of advanced histology in those with rapid growth (63.6%, 14/22, OR: 25.4; p<0.001). Polyp growth, but not patient age/sex or polyp morphology/location were significant predictors of advanced histology. CONCLUSION: Small 6-9 mm polyps present overall low risk to patients, with polyp growth strongly associated with higher risk lesions. Most patients (75%) with small 6-9 mm polyps will see polyp stability or regression, with advanced histology seen in only 7%. The minority of patients (25%) with small polyps that do grow have a 3-fold increased risk of advanced histology.

Radiologic T staging of colon cancer: renewed interest for clinical practice.

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. RESULTS. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults.

Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35]; P < .001). Conclusion Artificial intelligence-based profiling of body composition from routine abdominal CT scans identified myosteatosis as a key predictor of mortality risk in asymptomatic adults. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tong and Magudia in this issue.

Opportunistic Screening: Radiology Scientific Expert Panel.

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.

Beyond squamous cell carcinoma: MRI appearance of uncommon anal neoplasms and mimickers.

Anal cancer is an uncommon malignancy. In addition to squamous cell carcinoma, there are a variety of other less common malignancies and benign pathologies that may afflict the anal canal, with which abdominal radiologists should be familiar. Abdominal radiologists should be familiar with the imaging features that can help distinguish different rare anal tumors beyond squamous cell carcinoma and that can aid in diagnosis therefore help steer management. This review discusses these uncommon pathologies with a focus on their imaging appearance, management, and prognosis.

Quantification of Serum Metabolites in Early Colorectal Adenomas Using Isobaric Labeling Mass Spectrometry.

A major challenge in reducing the death rate of colorectal cancer is to screen patients using low-invasive testing. A blood test shows a high compliance rate with reduced invasiveness. In this work, a multiplex isobaric tag labeling strategy coupled with mass spectrometry is adopted to relatively quantify primary and secondary amine-containing metabolites in serum for the discovery of metabolite level changes of colorectal cancer. Serum samples from patients at different risk statuses and colorectal cancer growth statuses are studied. Metabolite identification is based on accurate mass matching and/or retention time of labeled metabolite standards. We quantify 40 metabolites across all the serum samples, including 18 metabolites validated with standards. We find significantly decreased levels of threonine and asparagine in the patients with growing adenomas or high-risk adenomas (p < 0.05). Glutamine levels decrease in patients with adenomas of unknown growth status or high-risk adenomas. In contrast, arginine levels are elevated in patients with low-risk adenoma. Receiver operating characteristic analysis shows high sensitivity and specificity of these metabolites for detecting growing adenomas. Based on these results, we conclude that a few metabolites identified here might contribute to distinguishing colorectal patients with growing adenomas from normal individuals and patients with unknown growth status of adenomas.

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnosis. The CADxDE includes material identification and machine learning (ML) based CADx. Benefits from DECT's capability of performing virtual monoenergetic imaging with the identified materials, the responses of different tissue types (e.g., muscle, water, and fat) in lesions at each energy can be explored by ML for CADx. Without losing essential factors in the DECT scan, a pre-log domain model-based iterative reconstruction is adopted to obtain decomposed material images, which are then used to generate the virtual monoenergetic images (VMIs) at selected n energies. While these VMIs have the same anatomy, their contrast distribution patterns contain rich information along with the n energies for tissue characterization. Thus, a corresponding ML-based CADx is developed to exploit the energy-enhanced tissue features for differentiating malignant from benign lesions. Specifically, an original image-driven multi-channel three-dimensional convolutional neural network (CNN) and extracted lesion feature-based ML CADx methods are developed to show the feasibility of CADxDE. Results from three pathologically proven clinical datasets showed 4.01% to 14.25% higher AUC (area under the receiver operating characteristic curve) scores than the scores of both the conventional DECT data (high and low energy spectrum separately) and the conventional CT data. The mean gain >9.13% in AUC scores indicated that the energy spectral-enhanced tissue features from CADxDE have great potential to improve lesion diagnosis performance.

CT without borders: Comparison of diagnoses for abdominal pain from a teaching hospital in rural Kenya and a US academic medical center.

PURPOSE: Radiology global health opportunities are expanding as more hospitals in low- and middle-income countries utilize CT. This creates opportunities for global health program building, education, service, and research. This study determines the diagnostic yield and variety of abdominopelvic CT diagnoses for abdominal pain in a US academic medical center (UW) compared to a rural Kenyan teaching hospital (Tenwek). METHODS: A retrospective, cross-sectional sequential sample of 750 adults from both hospitals who underwent abdominopelvic CT for abdominal pain from February 2019 through July 2020 was obtained. Exclusion criteria were trauma, cancer staging, and recent hospitalization or surgery. Patient age, sex, comparison studies, use of contrast, known cancer diagnosis, and CT diagnoses were compared. Negative exam rate, acute abdomen diagnosis, and new cancer diagnosis were recorded. Statistical analysis was performed using R. RESULTS: 750 UW patients met inclusion criteria (mean age 53.3 ± 20 years; 442 women) and 750 Tenwek patients met inclusion criteria (mean age 52.5 ± 18 years; 394 women). 72% of UW patients had comparison imaging compared to 6% of Tenwek patients. 11% (83/750) of UW patients had a known cancer diagnosis compared to 1% (10/750) of Tenwek patients. 39% of UW patients had a negative exam compared to 23% of Tenwek patients (p < 0.001). 58% of UW patients had an acute abdomen diagnosis compared to 38% of Tenwek patients (p < 0.001). 10 of the 15 top acute abdomen diagnoses were shared, but in different order of frequency. Diagnoses unique to UW were diverticulitis, constipation, stercoral colitis, and epiploic appendagitis. Diagnoses unique to Tenwek were tuberculosis and hydatidosis. 3% of UW patients received a new cancer diagnosis (7/19 metastatic), compared to 40% of Tenwek patients (153/303 metastatic) (p < 0.001). CONCLUSION: For adults undergoing CT for abdominal pain, there are differences in the prevalence of abdominal pain diagnoses, new cancer diagnosis, and negative exam rate between the rural Kenyan teaching hospital and the US academic medical center.