Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.

Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (P > .05). Quantitatively, medium-strength DLIR and high-strength DLIR yielded higher lesion-to-background contrast-to-noise ratios than ASIR-V 40 (medium-strength DLIR vs ASIR-V 40: odds ratio [OR], 1.96 [95% CI: 1.65, 2.33]; high-strength DLIR vs ASIR-V 40: OR, 5.36 [95% CI: 3.68, 7.82]; P < .001). Low-contrast lesion attenuation was reduced by 2.8-3.6 HU with DLIR. Interreader agreement was moderate to very good for the qualitative metrics. Subgroup analysis based on lesion size of larger than 1 cm and 1 cm or smaller yielded similar results (P > .05). Qualitatively, phantom study results were similar to those in patients (P > .05). Conclusion The detectability of low-contrast liver lesions was similar on CT scans reconstructed with low-, medium-, and high-strength DLIR and ASIR-V 40 in both patient and phantom studies. Lesion-to-background contrast-to-noise ratios were higher for DLIR medium- and high-strength reconstructions compared with ASIR-V 40. © RSNA, 2024 Supplemental material is available for this article.

Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques.

OBJECTIVES: To investigate intra-patient variability of iodine concentration (IC) between three different dual-energy CT (DECT) platforms and to test different normalization approaches. METHODS: Forty-four patients who underwent portal venous phase abdominal DECT on a dual-source (dsDECT), a rapid kVp switching (rsDECT), and a dual-layer detector platform (dlDECT) during cancer follow-up were retrospectively included. IC in the liver, pancreas, and kidneys and different normalized ICs (NICPV:portal vein; NICAA:abdominal aorta; NICALL:overall iodine load) were compared between the three DECT scanners for each patient. A longitudinal mixed effects analysis was conducted to elucidate the effect of the scanner type, scan order, inter-scan time, and contrast media amount on normalized iodine concentration. RESULTS: Variability of IC was highest in the liver (dsDECT vs. dlDECT 28.96 (14.28-46.87) %, dsDECT vs. rsDECT 29.08 (16.59-62.55) %, rsDECT vs. dlDECT 22.85 (7.52-33.49) %), and lowest in the kidneys (dsDECT vs. dlDECT 15.76 (7.03-26.1) %, dsDECT vs. rsDECT 15.67 (8.86-25.56) %, rsDECT vs. dlDECT 10.92 (4.92-22.79) %). NICALL yielded the best reduction of IC variability throughout all tissues and inter-scanner comparisons, yet did not reduce the variability between dsDECT vs. dlDECT and rsDECT, respectively, in the liver. The scanner type remained a significant determinant for NICALL in the pancreas and the liver (F-values, 12.26 and 23.78; both, p < 0.0001). CONCLUSIONS: We found tissue-specific intra-patient variability of IC across different DECT scanner types. Normalization mitigated variability by reducing physiological fluctuations in iodine distribution. After normalization, the scanner type still had a significant effect on iodine variability in the pancreas and liver. CLINICAL RELEVANCE STATEMENT: Differences in iodine quantification between dual-energy CT scanners can partly be mitigated by normalization, yet remain relevant for specific tissues and inter-scanner comparisons, which should be taken into account at clinical routine imaging. KEY POINTS: • Iodine concentration showed the least variability between scanner types in the kidneys (range 10.92-15.76%) and highest variability in the liver (range 22.85-29.08%). • Normalizing tissue-specific iodine concentrations against the overall iodine load yielded the greatest reduction of variability between scanner types for 2/3 inter-scanner comparisons in the liver and for all (3/3) inter-scanner comparisons in the kidneys and pancreas, respectively. • However, even after normalization, the dual-energy CT scanner type was found to be the factor significantly influencing variability of iodine concentration in the liver and pancreas.

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

OBJECTIVES: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. RESULTS: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. CONCLUSION: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. CLINICAL RELEVANCE STATEMENT: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. KEY POINTS: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.

CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.

OBJECTIVE: To quantitatively compare DLIR and ASiR-V with realistic anatomical images. METHODS: CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness. RESULTS: Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise. CONCLUSION: Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. KEY POINTS: • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.

Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Prediction of R Status in Resections for Pancreatic Cancer Using Simplified Radiological Criteria.

OBJECTIVE: Predicting R status before surgery for pancreatic cancer (PDAC) patients with upfront surgery and neoadjuvant therapy. SUMMARY BACKGROUND DATA: Negative surgical margins (R0) are a key predictor of long-term outcomes in PDAC. METHODS: Patients undergoing pancreatic resection with curative intent for PDAC were identified. Using the CT scans from the time of diagnosis, the 2019 NCCN borderline resectability criteria were compared to novel criteria: presence of any alteration of the superior mesenteric-portal vein (SMPV) and perivascular stranding of the superior mesenteric artery (SMA). Accuracy of predicting R status was evaluated for both criteria. Patient baseline characteristics, surgical, histopathological parameters, and long-term overall survival (OS) after resection were evaluated. RESULTS: A total of 593 patients undergoing pancreatic resections for PDAC between 2010 and 2018 were identified. Three hundred and twenty-five (54.8%) patients underwent upfront surgery, whereas 268 (45.2%) received neoadjuvant therapy. In upfront resected patients, positive SMA stranding was associated with 56% margin positive resection rates, whereas positive SMA stranding and SMPV alterations together showed a margin positive resection rate of 75%. In contrast to these criteria, the 2019 NCCN borderline criteria failed to predict margin status. In patients undergoing neoadjuvant therapy, only perivascular SMA stranding remained a predictor of margin positive resection, leading to a rate of 33% R+ resections. Perivascular SMA stranding was related to higher clinical T stage (P = 0.003) and clinical N stage (P = 0.043) as well as perineural invasion (P = 0.022). SMA stranding was associated with worse survival in both patients undergoing upfront surgery (36 vs 22 months, P = 0.002) and neoadjuvant therapy (47 vs 34 months, P = 0.050). CONCLUSIONS: The novel criteria were accurate predictors of R status in PDAC patients undergoing upfront resection. After neoadjuvant treatment, likelihood of positive resection margins is approximately halved, and only perivascular SMA stranding remained a predictive factor.

Concordance of EUS and MRI/MRCP findings among high-risk individuals undergoing pancreatic cancer screening.

BACKGROUND/OBJECTIVES: Surveillance with endoscopic ultrasonography (EUS) and MRI/magnetic retrograde cholangiopancreatography (MRCP) is recommended for individuals at high risk for pancreatic cancer. We sought to characterize the findings of these surveillance exams and define the level of concordance between these two modalities. METHODS: 173 asymptomatic high-risk individuals (HRIs) meeting criteria for pancreatic cancer surveillance underwent EUS, MRI/MRCP, or both between 2008 and 2021. Clinical records were reviewed in all cases. RESULTS: HRIs underwent an average of 3.6 ± 3.2 surveillance exams over a period of 3.3 ± 3.5 years. Abnormalities including intraductal papillary mucinous neoplasms (IPMNs), solid lesions, and parenchymal irregularities were identified in 50.9% (n = 88). Four of these abnormalities (2.3%) had worrisome features, defined by cyst size, thickened/enhancing cyst walls, rapid growth rate, or change in main pancreatic duct diameter. All four worrisome lesions were seen on both MRI/MRCP and EUS. No pancreatic cancers were detected. Baseline EUS and MRI/MRCP exams were compared in 106 patients for concordance, and most (n = 66, 62.3%) were concordant. High levels of concordance were specifically observed for a dilated main pancreatic duct (p < 0.01) and cystic lesions >5 mm (p = 0.01). Among discordant cases, most (30/40; 75%) involved abnormal tissue heterogeneity seen primarily on EUS. None of these discordant lesions ultimately developed worrisome features. CONCLUSIONS: Worrisome pancreatic lesions were uncommon in our high-risk pancreatic cancer population and were detected by both EUS and MRI/MRCP. There was mild discordance with respect to less worrisome findings, but these discrepancies were not associated with any adverse clinical outcomes.

Arterial involvement and resectability scoring system to predict R0 resection in patients with pancreatic ductal adenocarcinoma treated with neoadjuvant chemoradiation therapy.

OBJECTIVES: To derive a CT-based scoring system incorporating arterial involvement and resectability status to predict R0 resection in patients with pancreatic ductal adenocarcinoma (PDAC) undergoing neoadjuvant chemoradiation therapy (CRT). METHODS: This retrospective study included 112 patients with PDAC who underwent dynamic contrast-enhanced CT before and after neoadjuvant CRT. A 5-point score was used to determine arterial involvement (A score; 1 = no involvement, 2 = haziness, 3 = abutment, 4 = encasement, 5 = deformity) and 4-point score evaluating resectability status (R score; 1 = resectable, 2 = borderline resectable [BR] with venous involvement, 3 = BR with arterial involvement, 4 = locally advanced [LA]). A score before and after CRT were summed with R score before and after CRT to compute the AR score (ARtotal). The associations between ARtotal, R0 resection, overall survival (OS), and disease-free survival (DFS) were assessed. RESULTS: The ARtotal was associated with R0 resection (p < .001) and showed area under the ROC curve of 0.79 for differentiating R0 and R1 resections. Median OS was significantly lower for patients with ARtotal  > 9 (median: 35.2 months) compared to patients with ARtotal ≤ 9 (median: not estimable) (p < .001). Similar results were observed for DFS (median, 16.8 months in > 9 vs median, not estimable in ≤ 9; p < .001). CONCLUSIONS: A composite score which incorporates degree of arterial involvement and resectability status before and after neoadjuvant CRT is associated with R0 resection and discriminates between R0 and R1 resections in PDAC. KEY POINTS: • A scoring system incorporating arterial involvement and resectability status was associated with R0 resection. • ARtotal > 9 could predict patients' overall and disease-free survival.

Renal Lesion Characterization by Dual-Layer Dual-Energy CT: Comparison of Virtual and True Unenhanced Images.

BACKGROUND. Prior studies have provided mixed results for the ability to replace true unenhanced (TUE) images with virtual unenhanced (VUE) images when characterizing renal lesions by dual-energy CT (DECT). Detector-based dual-layer DECT (dlDECT) systems may optimize performance of VUE images for this purpose. OBJECTIVE. The purpose of this article was to compare dual-phase dlDECT examinations evaluated using VUE and TUE images in differentiating cystic and solid renal masses. METHODS. This retrospective study included 110 patients (mean age, 64.3 ± 11.8 years; 46 women, 64 men) who underwent renal-mass protocol dlDECT between July 2018 and February 2022. TUE, VUE, and nephrographic phase image sets were reconstructed. Lesions were diagnosed as solid masses by histopathology or MRI. Lesions were diagnosed as cysts by composite criteria reflecting findings from MRI, ultrasound, and the TUE and nephrographic phase images of the dlDECT examinations. One radiologist measured lesions' attenuation on all dlDECT image sets. Lesion characterization was compared between use of VUE and TUE images, including when considering enhancement of 20 HU or greater to indicate presence of a solid mass. RESULTS. The analysis included 219 lesions (33 solid masses; 186 cysts [132 simple, 20 septate, 34 hyperattenuating]). TUE and VUE attenuation were significantly different for solid masses (33.4 ± 7.1 HU vs 35.4 ± 8.6 HU, p = .002), simple cysts (10.8 ± 5.6 HU vs 7.1 ± 8.1 HU, p < .001), and hyperattenuating cysts (56.3 ± 21.0 HU vs 47.6 ± 16.3 HU, p < .001), but not septate cysts (13.6 ± 8.1 HU vs 14.0 ± 6.8 HU, p = .79). Frequency of enhancement 20 HU or greater when using TUE and VUE images was 90.9% and 90.9% in solid masses, 0.0% and 9.1% in simple cysts, 15.0% and 10.0% in septate cysts, and 11.8% and 38.2% in hyperattenuating cysts. All solid lesions were concordant in terms of enhancement 20 HU or greater when using TUE and VUE images. Twelve simple cysts and nine hyperattenuating cysts showed enhancement of 20 HU or greater when using VUE but not TUE images. CONCLUSION. Use of VUE images reliably detected enhancement in solid masses. However, VUE images underestimated attenuation of simple and hyperattenuating cysts, leading to false-positive findings of enhancement by such lesions. CLINICAL IMPACT. The findings do not support replacement of TUE acquisitions with VUE images when characterizing renal lesions by dlDECT.

Multi-detector computed tomography (MDCT)-based severity score as a prognostic tool in patients with suspected immune checkpoint inhibitor therapy associated colitis.

OBJECTIVE: This study aimed to assess MDCT as a diagnostic and prognostic tool in patients with suspected immune checkpoint inhibitor (ICI)-related colitis. MATERIALS AND METHODS: This retrospective cohort study included patients receiving ICIs at three hospitals between 2015 and 2019 who underwent both abdominopelvic MDCT and endoscopic biopsy to workup suspected ICI-related colitis. Two radiologists independently reviewed MDCT images for signs of colitis based on pre-defined features. Diagnostic performance of MDCT was calculated and categorical variables between treatment subgroups were compared. Logistic regression was used to develop proposed MDCT criteria for diagnosis and MDCT severity score based on a combination of MDCT features of colitis to predict the patient outcomes in ICI-related colitis. RESULTS: A total of 118 MDCT scans from 108 patients were evaluated for suspected colitis, with 72 confirmed ICI-related colitis cases. Sensitivity, specificity, PPV, and NPV of MDCT for diagnosis of ICI-related colitis was 81% (58/72), 52 % (24/46), 73% (58/80), and 63% (24/38), respectively. Small bowel involvement was visualized in 25% of cases with ICI-related colitis (18/72). In melanoma patients presenting with diarrhea grade ≥ 2 (n = 40), MDCT had the best diagnostic performance for ICI-related colitis (specificity = 80% [8/10], PPV = 92% [23/25]). MDCT severity scores predicted intravenous steroid use (OR 10.3, p = 0.004), length of stay > 7 days (OR 9.0, p < 0.001), and endoscopic mucosal ulceration (OR 4.7, p = 0.02). CONCLUSION: MDCT is a useful diagnostic and prognostic tool for evaluating patients with immune checkpoint inhibitor-related colitis. An MDCT-based severity score enables assessment of disease severity and predicts outcome. KEY POINTS: • MDCT is useful for the diagnosis of colitis in patients receiving immune checkpoint inhibitor (ICI) therapy, and an MDCT-based severity score allows for prognostication of patient outcomes. • MDCT yielded moderate sensitivity (81%) for diagnosis of ICI-related colitis but limited specificity (52%). However, in symptomatic melanoma patients (grade 2-4 diarrhea) with a high pretest probability, MDCT proved useful for diagnosis with a high PPV (92%). • For ICI-related colitis, our proposed MDCT severity score has prognostic value in predicting intravenous steroid use, prolonged length of stay during inpatient admission (> 7 days), and endoscopic mucosal ulceration.

Does CT overestimate extra-pancreatic perineural invasion in patients with pancreatic ductal adenocarcinoma following neoadjuvant chemoradiation therapy?

OBJECTIVES: To evaluate the diagnostic performance of CT in the assessment of extra-pancreatic perineural invasion (EPNI) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective study included 123 patients (66 men; median age, 66 years) with PDAC who underwent radical surgery and pancreatic protocol CT for assessing surgical resectability between September 2011 and March 2019. Among the 123 patients, 97 patients had received neoadjuvant chemoradiation therapy (CRT). Two radiologists reviewed the CT images for evidence of EPNI using a 5-point scale (5 = definitely present, 4 = probably present, 3 = equivocally present, 2 = probably absent, and 1 = definitely absent). Diagnostic performance for assessing EPNI was evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS: The sensitivity, specificity, and area under the ROC curve for assessing EPNI were 98%, 30%, and 0.62 in all patients; 97%, 22%, and 0.59 in patients with neoadjuvant CRT; and 100%, 100%, and 1.00 in patients without neoadjuvant CRT, respectively. False-positive assessment of EPNI occurred in 23% of patients (n = 28/123), and 100% of these (n = 28/28) had received neoadjuvant CRT. There was moderate to substantial agreement between the readers (ĸ = 0.49-0.62). CONCLUSION: Pancreatic protocol CT has better diagnostic performance for determination of EPNI in treatment naïve patients with PDAC and overestimation of EPNI is likely in patients who have received preoperative CRT. ADVANCES IN KNOWLEDGE: Pancreatic protocol CT has better diagnostic performance for the detection of EPNI in treatment naïve patients compared to patients receiving neoadjuvant CRT.

Diagnostic performance and feasibility of dual-layer detector dual-energy CT for characterization of urinary stones in patients of different sizes.

BACKGROUND: Urinary stones are frequently encountered in urology and are typically identified using non-contrast CT scans. Dual-energy CT (DECT) is a valuable imaging technique that produces material-specific images and allows for precise assessment of stone composition by estimating the effective atomic number (Zeff), a capability not achievable with the conventional single-energy CT's attenuation measurement method. PURPOSE: To investigate the diagnostic performance and image quality of dual-layer detector DECT (dlDECT) in characterizing urinary stones in patients of different sizes. METHODS: All consecutive dlDECT examinations with stone protocol and presence of urinary stones between July 2018 and November 2019 were retrospectively evaluated. Two radiologists independently reviewed 120 kVp and color-overlay Zeff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Zeff values measurement. RESULTS: A total of 739 urinary stones (median size 3.7 mm, range 1-35 mm) were identified on 177 CT examinations from 155 adults (mean age, 57 ± 15 years, 80 men, median weight 82.6 kg, range 42.6-186.9 kg). Using color-overlay Zeff images, the radiologists could subjectively interpret the composition in all stones ≥ 3 mm (n = 491). For stones with available reference standards (n = 74), dlDECT yielded a sensitivity of 80% (95%CI 44-98%) and a specificity of 98% (95%CI 92-100%) in visually discriminating uric acid from non-uric acid stones. Patients weighing > 90 kg and ≤ 90 kg had similar stone characterizability (p = 0.20), with 86% of stones characterized in the > 90 kg group and 87% in the ≤ 90 kg group. All examinations throughout various patients' weights revealed acceptable image quality. A Zeff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Zeff analysis revealed AUCs of 0.78 and 0.91 for differentiating calcium-based stones from other non-uric stones and all stone types, respectively. CONCLUSION: dlDECT allowed accurate differentiation of uric acid and non-uric acid stones among patients with different body sizes with acceptable image quality. CLINICAL IMPACT: The ability to accurately differentiate uric acid stones from non-uric acid stones using color-overlay Zeff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care.

Material density dual-energy CT images: value added in early diagnosis of peritoneal carcinomatosis : Original research.

OBJECTIVE: To assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). MATERIALS AND METHODS: Thirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant. RESULTS: 65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13-23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m2. 65 keV + MD images showed higher diagnostic confidence, lesion conspicuity, and lesion sharpness for the experienced reader (p < 0.001). CNR was higher in MD images (1.7 ± 0.5) than 65 keV images (0.1 ± 0.02, p < 0.001). All readers showed moderate interobserver agreement for determining PC by both protocols (κ = 0.58 and κ = 0.47). CONCLUSION: MD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus.

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

BACKGROUND: CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. OBJECTIVE: To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). METHODS: This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). RESULTS: Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). CONCLUSION: DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. CLINICAL IMPACT: Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

Attenuation values on virtual unenhanced images obtained with detector-based dual-energy computed tomography: observations on single- and split-bolus contrast protocols.

PURPOSE: To compare virtual unenhanced (VUE) attenuation values and their agreement with true unenhanced (TUE) images in patients who underwent dual-layer detector-based dual-energy computed tomography (dlDECT) with single- vs. split-bolus contrast media protocol. METHODS: In this HIPAA-compliant, IRB-approved retrospective analysis, a total of 105 patients who underwent nephrographic phase (NP) dlDECT between 07/2018 and 11/2019 were included: 55 patients received single bolus and 50 patients split-bolus examinations. Both scan protocols comprised a TUE and 120-kVp NP acquisition from which VUE images were reconstructed. A radiologist performed ROI-based attenuation measurements of liver parenchyma, main portal vein, aorta, spleen, renal parenchyma, and pelvis on TUE and VUE images. Agreement between TUE and VUE images was determined and compared for both protocols and each anatomic region. RESULTS: VUE attenuation was significantly higher than TUE attenuation in both cohorts in the liver, portal vein, spleen, and renal parenchyma (p < 0.05), while it was similar in the abdominal aorta in both cohorts (p = 0.05, 0.7522, respectively). VUE attenuation was significantly higher than TUE attenuation in the renal pelvis of the split-bolus cohort (p < 0.05). When comparing VUE images between single- and split-bolus protocols, the renal parenchyma yielded a significantly higher VUE attenuation in the single-bolus cohort (single bolus: 38.8 ± 3.3 HU vs. split bolus: 36.8 ± 3.6 HU; p < 0.05), whereas the split-bolus cohort revealed markedly higher VUE attenuation in the renal pelvis (single bolus: 2.3 ± 10.8 HU vs. split bolus: 92.3 ± 76.8; p < 0.05). Mean intra-patient differences between TUE and VUE images were comparable between single- and split-bolus cohorts (p-range 0.09-0.35) except for the renal parenchyma and pelvis: in the first, the single-bolus cohort yielded a higher VUE attenuation, while in the second, attenuation was significantly higher in the split-bolus cohort (p < 0.05). CONCLUSION: VUE attenuation overestimated TUE attenuation and differed between split- and single-bolus protocols for the renal parenchyma and pelvis, while all other tissues showed comparable VUE attenuation.

Virtual Unenhanced Images: Qualitative and Quantitative Comparison Between Different Dual-Energy CT Scanners in a Patient and Phantom Study.

MATERIALS AND METHODS: Forty-four patients with clinical contrast-enhanced abdominal examinations on each of the 3 DECT scanner types and a phantom scanned with the same protocols were included in this retrospective study. Qualitative and quantitative assessment was performed on VUE images. Quantitative evaluation included measurement of attenuation and image noise for various tissues and the phantom. Virtual unenhanced image attenuation and noise were compared between scanner types, and intrapatient interscanner reproducibility of virtual unenhanced image attenuation was calculated as the percentage of measurement pairs with an interscanner difference ≤ 10 HU. Image quality, noise, sharpness, and iodine elimination were assessed qualitatively by 2 radiologists. RESULTS: Significant interscanner differences in VUE attenuation and noise were found in all tissues. dlDECT and rsDECT showed significantly higher VUE attenuation than dsDECT in the aorta, portal vein, and kidneys (P < 0.05). Conversely, VUE attenuation in dsDECT was significantly higher than in dlDECT/rsDECT for subcutaneous and retroperitoneal fat (both P < 0.05). A total of 91.9% (385/419) of measurements were reproducible between rsDECT and dlDECT, 70.9% (297/419) between dsDECT and rsDECT, and 66.8% (280/419) between dsDECT and dlDECT. Virtual unenhanced image attenuation in the contrast media-filled phantom cavity was 12.7 ± 4.7 HU in dlDECT, -5.3 ± 4.2 HU in rsDECT, and -4.0 ± 10.7 HU in dsDECT with significant differences between dlDECT and rsDECT/dsDECT, respectively (P < 0.05), between which attenuation was comparable in the unenhanced extraluminal phantom component (P = 0.11-0.62). Qualitatively, dsDECT yielded best iodine elimination, whereas sharpness, image noise, and overall image quality were rated higher in dlDECT and rsDECT. CONCLUSIONS: There are significant interscanner differences in the attenuation measurements and qualitative assessment of VUE images, which should be acknowledged when using these images in patients that are being scanned on different DECT scanner types during imaging follow-up.

The absolute tumor-capsule contact length in the diagnosis of extraprostatic extension of prostate cancer.

BACKGROUND: Extraprostatic extension (EPE) of prostate cancer is associated with a poor prognosis. The broad-based capsule-tumor interface has been recognized as one of the worrisome imaging features in multiparametric prostate MRI (mpMRI). However, there was significant heterogeneity among the measurement method used in prior studies. OBJECTIVES: This study's objectives were to investigate and compare the accuracy between the curvilinear and linear measurement, find the optimal cut-off contact surface threshold for the diagnosis of EPE, and assess the benefit of the additional contact surface measurement versus visual assessment alone. METHODS: The status of EPE in mpMRI and the overall PI-RADS were assessed. The tumor's dimensions, the actual tumor-capsule contact length (ACTCL), and the absolute tumor-capsule contact length (ABTCL) were measured. The parameters were analyzed and correlated with the EPE status from prostatectomy specimens. RESULTS: Ninety-five patients who underwent mpMRI followed by prostatectomy were included in the study. High Gleason score (score 8-9), radiologist's impression of EPE, and PI-RADS 5 were significantly correlated with EPE in surgical specimens (p = 0.014, p < 0.001, and p < 0.001, respectively). Both ACTCL and ABTCL of patients with EPE were significantly higher than those without EPE in all imaging sequences (p < 0.001 to p = 0.003). The ABTCL has higher accuracy than the ACTCL. Dynamic contrast enhancement (DCE) was the most accurate sequence to measure the contact interface. The recommended cut-off value of ABTCL was 15.0 mm, which had a sensitivity and specificity of 75.86% and 72.09%. Multivariable analysis revealed that the ABTCL > 15 mm and the radiologist's impression on visual assessment were the only two independent predictors for the prediction of EPE (p = 0.048 and p = 0.016, respectively). Improvement of diagnostic performance was achieved when the two factors were combined. CONCLUSION: The ABTCL has better accuracy than the curvilinear measurement in the prediction of EPE. The optimum sequence for the measurement of the contact surface is the DCE. We recommended using 15.0 mm as a cut-off point. CLINICAL IMPACT: The addition of the ABTCL measurement showed an increase in diagnostic performance. We encourage radiologists to use the capsular contact measurement in addition to their visual assessment to detect EPE in pre-operative MRI.

Association of body composition parameters measured on CT with risk of hospitalization in patients with Covid-19.

PURPOSE: To assess prognostic value of body composition parameters measured at CT to predict risk of hospitalization in patients with COVID-19 infection. METHODS: 177 patients with SARS-CoV-2 infection and with abdominopelvic CT were included in this retrospective IRB approved two-institution study. Patients were stratified based on disease severity as outpatients (no hospital admission) and patients who were hospitalized (inpatients). Two readers blinded to the clinical outcome segmented axial CT images at the L3 vertebral body level for visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), muscle adipose tissue (MAT), muscle mass (MM). VAT to total adipose tissue ratio (VAT/TAT), MAT/MM ratio, and muscle index (MI) at L3 were computed. These measures, along with detailed clinical risk factors, were compared in patients stratified by severity. Various logistic regression clinical and clinical + imaging models were compared to discriminate inpatients from outpatients. RESULTS: There were 76 outpatients (43%) and 101 inpatients. Male gender (p = 0.013), age (p = 0.0003), hypertension (p = 0.0003), diabetes (p = 0.0001), history of cardiac disease (p = 0.007), VAT/TAT (p < 0.0001), and MAT/MM (p < 0.0001), but not BMI, were associated with hospitalization. A clinical model (age, gender, BMI) had AUC of 0.70. Addition of VAT/TAT to the clinical model improved the AUC to 0.73. Optimal model that included gender, BMI, race (Black), MI, VAT/TAT, as well as interaction between gender and VAT/TAT and gender and MAT/MM demonstrated the highest AUC of 0.83. CONCLUSION: MAT/MM and VAT/TAT provides important prognostic information in predicting patients with COVID-19 who are likely to require hospitalization.

Diagnostic utility of CT for suspected immune checkpoint inhibitor enterocolitis.

BACKGROUND AND AIMS: Immune checkpoint inhibitor (ICI) enterocolitis is a common immune-related adverse event and can be fatal, especially when not diagnosed and treated promptly. The current gold standard for diagnosis is endoscopy with biopsy, but CT scan is a possible alternative. The primary objective of this study is to identify the diagnostic performance of CT in the evaluation of ICI enterocolitis. METHODS: With institutional review board approval, we conducted a retrospective cohort study of patients who received ICI therapy between 2015 and 2019 across a healthcare system. Patients were included if they underwent both abdominal CT and endoscopy with biopsy within 3 days. The radiological and pathological diagnoses, as well as clinical characteristics, were extracted from the electronic medical record. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CT for diagnosing ICI enterocolitis when compared with tissue diagnosis. RESULTS: Of the 4474 patients screened, 138 met inclusion criteria. Most common tumor types were melanoma (37%) and lung cancer (19%). Seventy-four per cent were treated with antiprogrammed cell death (PD-1)/PD-L1 therapy. Thirty-nine per cent had signs of enterocolitis on CT scan and 58% had biopsy-proven ICI enterocolitis. Sensitivity and specificity of CT were 50% and 74%, respectively. PPV was 73% and NPV was 52%. Of those with confirmed ICI enterocolitis, 70% had grade 3 or higher symptoms, 91% received steroids and 40% received infliximab. CONCLUSION: The performance of CT scan for diagnosis of ICI enterocolitis is moderate to poor and does not replace endoscopy with biopsy.