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Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.

Background The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. Purpose To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions. Material and Methods A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II–IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions. Results The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934–0.979) as well as in the test dataset (AUCs = 0.892–0.962) of cohort B ( P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images ( P = 0.038) in the training dataset of cohort B. Conclusion No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR.The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p0.001).DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR.• Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (1 cm).

This study aimed to understand the relationship of preoperative measurements and risk factors on operative time and outcomes of laparoscopic donor nephrectomy. Two hundred forty-two kidney donors between 2010 and 2017 were identified. Patients' demographic, anthropomorphic, and operative characteristics were abstracted from the electronic medical record. Glomerular filtration rates (GFR) were documented before surgery, within 24 hours, 6, 12, and 24 months after surgery. Standard radiological measures and kidney volumes, and subcutaneous and perinephric fat thicknesses were assessed by three radiologists. Data were analyzed using standard statistical measures. There was significant correlation between cranio-caudal and latero-lateral diameters (P < .0001) and kidney volume. The left kidney was transplanted in 92.6% of cases and the larger kidney in 69.2%. Kidney choice (smaller vs. larger) had no statistically significant impact on the rate of change of donor kidney function over time adjusting for age, sex and race (P = .61). Perinephric fat thickness (+4.08 minutes) and surgery after 2011 were significantly correlated with operative time (P ≤ .01). In conclusion, cranio-caudal diameters can be used as a surrogate measure for volume in the majority of donors. Size may not be a decisive factor for long-term donor kidney function. Perinephric fat around the donor kidney should be reported to facilitate operative planning.

Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer.We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient heightThere was strong agreement between manual and automatic segmentation, with PPMCCs 0.90 and ICC3s 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods.Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.

To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics.For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence.The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Background Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. Methods Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). Results A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%). Conclusion This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

To assess the impact of radiology review for discordance between pathology results from computed tomography (CT)-guided biopsies versus imaging findings performed before a biopsy.In this retrospective review, which is compliant with the Health Insurance Portability and Accountability Act and approved by the institutional review board, 926 consecutive CT-guided biopsies performed between January 2015 and December 2017 were included. In total, 453 patients were presented in radiology review meetings (prospective group), and the results were classified as concordant or discordant. Results from the remaining 473 patients not presented at the radiology review meetings were retrospectively classified. Times to reintervention and to definitive diagnosis were obtained for discordant cases; of these, 49 (11%) of the 453 patients were in the prospective group and 55 (12%) of the 473 patients in the retrospective group.Pathology results from CT-guided biopsies were discordant with imaging in 11% (104/926) of the cases, with 57% (59/104) of these cases proving to be malignant. In discordant cases, reintervention with biopsy and surgery yielded a shorter time to definitive diagnosis (28 and 14 days, respectively) than an imaging follow-up (78 days) (P.001). The median time to diagnosis was 41 days in the prospective group and 56 days in the retrospective group (P = .46). When radiologists evaluated the concordance between pathology and imaging findings and recommended a repeat biopsy for the discordant cases, more biopsies were performed (50% [11/22] vs 13% [4/31]; P = .005).Eleven percent of CT-guided biopsies yielded pathology results that were discordant with imaging findings, with 57% of these proving to be malignant on further workup.

Commonly encountered in the general population, in the vast majority of cases nonhereditary developmental liver cysts are asymptomatic, not associated with altered hepatic function and confidently diagnosed on imaging studies, and do not require further workup, follow-up, or treatment. However, particularly in women, simple hepatic cysts may reach large sizes and cause symptoms and signs resulting from mass effect, vascular compression, and biliary obstruction. Furthermore, although rarely compared to the incidence observed in patients with adult polycystic kidney and liver disease, sporadic hepatic cysts sometimes undergo life-threatening complications such as intracystic hemorrhage, infection, or rupture, which require prompt imaging triage and appropriate interventional, laparoscopic, or open surgical treatment. This pictorial essay reviews with examples the cross-sectional imaging findings of symptomatic and complicated nonhereditary liver cysts, aiming to provide radiologists with an increased familiarity with these uncommon, challenging occurrences. Emphasis is placed on the role of MRI as a useful problem-solving modality to elucidate the complex imaging appearances resulting from intracystic bleeding and superinfection, and to differentiate complicated cysts from other hemorrhagic liver lesions and biliary cystic tumors.

We read with interest the comprehensive review article on contrast-induced nephrotoxicity (CIN) by Nicola et al in Current Problems in Diagnostic radiology, including the most recent controversies about CIN. When discussing the role of risk factors for CIN, the authors state that “owing to the cumulative effects of multiple risk factors, an assessment tool is needed to estimate the possible level of damage,” and introduce the risk stratification score developed by Mehran et al from a large cohort of patient who underwent coronary angiography, which may predict the risk of CIN and of dialysis. Published in 2004, the Mehran risk stratification system (MRSS) involves a CIN score sheet based upon the presence of multiple risk factors including hypotension, intra-aortic balloon pump, congestive heart failure, chronic kidney disease (CKD), advanced age, anemia, diabetes, and contrast medium volume. At most general hospitals, some of these conditions are very common in hospitalized patients who undergo contrast-enhanced computed tomography (CT) for investigation of several different disorders. Therefore, at our department we tried to prospectively assess the usefulness of the MRSS in inpatients who received contrastenhanced body CT and had sufficient clinical and laboratory follow-up during hospitalization. Whereas the MRSS is validated in patients receiving intra-arterial contrast medium during coronary catheterization, to the best of our knowledge its use in CT has not been previously reported in literature. The study lasted 2 months and included 92 patients with estimated glomerular filtration rate (eGFR) below 60 mL/min and 71 matched controls with normal baseline renal function and similar age, sex distribution, and prevalence of comorbidities. The CKD group underwent CIN prophylaxis according to the European Society of Urogenital Radiology guidelines, including isotonic saline or sodium bicarbonate hydration, administration of N-acetylcysteine, and discontinuation of nephrotoxic medications. Standard weight-adjusted doses of low-osmolar contrast agents (either iopromide 370 mg I/mL or iomeprol 350 mg I/mL) were administered.