External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.

The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest. PURPOSE: To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort. METHODS: Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity. RESULTS: A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%). CONCLUSION: The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.

CT-defined sarcopenia predicts treatment response in primary central nervous system lymphomas.

OBJECTIVE: Body composition assessment derived from cross-sectional imaging has shown promising results as a prognostic biomarker in several tumor entities. Our aim was to analyze the role of low skeletal muscle mass (LSMM) and fat areas for prognosis of dose-limiting toxicity (DLT) and treatment response in patients with primary central nervous system lymphoma (PCNSL). METHODS: Overall, 61 patients (29 female patients, 47.5%) with a mean age of 63.8 ± 12.2 years, range 23-81 years, were identified in the data base between 2012 and 2020 with sufficient clinical and imaging data. Body composition assessment, comprising LSMM and visceral and subcutaneous fat areas, was performed on one axial slice on L3-height derived from staging computed tomography (CT) images. DLT was assessed during chemotherapy in clinical routine. Objective response rate (ORR) was measured on following magnetic resonance images of the head accordingly to the Cheson criteria. RESULTS: Twenty-eight patients had DLT (45.9%). Regression analysis revealed that LSMM was associated with objective response, OR = 5.19 (95% CI 1.35-19.94, p = 0.02) (univariable regression), and OR = 4.23 (95% CI 1.03- 17.38, p = 0.046) (multivariable regression). None of the body composition parameters could predict DLT. Patients with normal visceral to subcutaneous ratio (VSR) could be treated with more chemotherapy cycles compared to patients with high VSR (mean, 4.25 vs 2.94, p = 0.03). Patients with ORR had higher muscle density values compared to patients with stable and/or progressive disease (34.46 ± vs 28.18 ± HU, p = 0.02). CONCLUSIONS: LSMM is strongly associated with objective response in patients with PCNSL. Body composition parameters cannot predict DLT. CLINICAL RELEVANCE STATEMENT: Low skeletal muscle mass on computed tomography (CT) is an independent prognostic factor of poor treatment response in central nervous system lymphoma. Analysis of the skeletal musculature on staging CT should be implemented into the clinical routine in this tumor entity. KEY POINTS: • Low skeletal muscle mass is strongly associated with the objective response rate. • No body composition parameters could predict dose-limiting toxicity.

Feasibility of spectral CT-derived extracellular volume fraction for differentiating aldosterone-producing from nonfunctioning adrenal nodules.

OBJECTIVE: To assess the feasibility of spectral CT-derived extracellular volume (ECV) for differentiating aldosterone-producing nodules (APN) from nonfunctioning adrenal nodules (NFN). METHODS: Sixty-nine patients with biochemically and histologically confirmed unilateral APN (34) and NFN (35) as well as 23 patients with bilateral APN (19) and NFN (27) confirmed biochemically and by adrenal vein sampling (AVS) were enrolled in this retrospective study from October 2020 to April 2022. All patients underwent contrast-enhanced spectral CT of the adrenal glands with a 10-min delayed phase. The haematocrit level was measured within 2 days of CT. An iodine density map was derived from the delayed CT. The ECV fractions of the APN and NFN were calculated and compared in the test cohort of 69 patients with unilateral adrenal nodules. The optimal cut-off value was determined to evaluate the diagnostic efficacy of the ECV fraction for differentiating APN from NFN in the validation cohort of 23 patients with bilateral adrenal nodules. RESULTS: The ECV fractions of the APN (11.17 ± 4.57%) were significantly lower (p < 0.001) than that of the NFN (24.79 ± 6.01%) in the test cohort. At cut-off ECV value of 17.16%, the optimal area under the receiver operating characteristic curve was 0.974 (95% confidence interval: 0.942-1) with 91.4% sensitivity, 93.9% specificity, and 92.8% accuracy in the test cohort and 89.5% sensitivity, 96.3% specificity, and 93.5% accuracy in the validation cohort for differentiating APN from NFN. CONCLUSION: The spectral CT-derived ECV fraction can differentiate APN from NFN with high diagnostic performance. CLINICAL RELEVANCE STATEMENT: Spectral CT-derived extracellular volume fraction could accurately differentiate between adrenal aldosterone-producing nodules and nonfunctioning nodules. It might serve as a noninvasive alternative to adrenal vein sampling in primary aldosteronism patients with bilateral adrenal nodules. KEY POINTS: • Conventional CT cannot differentiate aldosterone-producing adrenal nodules from nonfunctioning nodules. • Extracellular volume of adrenal aldosterone-producing nodules was significantly lower than that of nonfunctioning nodules and normal adrenal glands. It can accurately differentiate between aldosterone-producing and nonfunctioning adrenal nodules. • Extracellular volume may be a novel, noninvasive biomarker alternative to adrenal vein sampling for determining the functional status of bilateral adrenal nodules in patients with primary aldosteronism.

Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.

OBJECTIVES: To evaluate the prognostic value of CT-based markers of sarcopenia and myosteatosis in comparison to the Eastern Cooperative Oncology Group (ECOG) score for survival of patients with advanced pancreatic cancer treated with high-intensity focused ultrasound (HIFU). MATERIALS AND METHODS: For 142 retrospective patients, the skeletal muscle index (SMI), skeletal muscle radiodensity (SMRD), fatty muscle fraction (FMF), and intermuscular fat fraction (IMFF) were determined on superior mesenteric artery level in pre-interventional CT. Each marker was tested for associations with sex, age, body mass index (BMI), and ECOG. The prognostic value of the markers was examined in Kaplan-Meier analyses with the log-rank test and in uni- and multivariable Cox proportional hazards (CPH) models. RESULTS: The following significant associations were observed: Male patients had higher BMI and SMI. Patients with lower ECOG had lower BMI and SMI. Patients with BMI lower than 21.8 kg/m2 (median) also showed lower SMI and IMFF. Patients younger than 63.3 years (median) were found to have higher SMRD, lower FMF, and lower IMFF. In the Kaplan-Meier analysis, significantly lower survival times were observed in patients with higher ECOG or lower SMI. Increased patient risk was observed for higher ECOG, lower BMI, and lower SMI in univariable CPH analyses for 1-, 2-, and 3-year survival. Multivariable CPH analysis for 1-year survival revealed increased patient risk for higher ECOG, lower SMI, lower IMFF, and higher FMF. In multivariable analysis for 2- and 3-year survival, only ECOG and FMF remained significant. CONCLUSION: CT-based markers of sarcopenia and myosteatosis show a prognostic value for assessment of survival in advanced pancreatic cancer patients undergoing HIFU therapy. CLINICAL RELEVANCE STATEMENT: The results indicate a greater role of myosteatosis for additional risk assessment beyond clinical scores, as only FMF was associated with long-term survival in multivariable CPH analyses along ECOG and also showed independence to ECOG in group analysis. KEY POINTS: • This study investigates the prognostic value of CT-based markers of sarcopenia and myosteatosis for patients with pancreatic cancer treated with high-intensity focused ultrasound. • Markers for sarcopenia and myosteatosis showed a prognostic value besides clinical assessment of the physical status by the Eastern Cooperative Oncology Group score. In contrast to muscle size measurements, the myosteatosis marker fatty muscle fraction demonstrated independence to the clinical score. • The results indicate that myosteatosis might play a greater role for additional patient risk assessments beyond clinical assessments of physical status.

An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.

OBJECTIVES: To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. METHODS: A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature. RESULTS: The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. CONCLUSIONS: Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. CLINICAL RELEVANCE STATEMENT: The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. KEY POINTS: • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.

OBJECTIVES: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). METHODS: This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. RESULTS: A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. CONCLUSION: State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. CLINICAL RELEVANCE: Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. KEY POINTS: • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.

A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.

OBJECTIVES: To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS: LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS: Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION: Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT: Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS: • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.

Large variation in radiation dose for routine abdomen CT: reasons for excess and easy tips for reduction.

OBJECTIVE: To characterize the use and impact of radiation dose reduction techniques in actual practice for routine abdomen CT. METHODS: We retrospectively analyzed consecutive routine abdomen CT scans in adults from a large dose registry, contributed by 95 hospitals and imaging facilities. Grouping exams into deciles by, first, patient size, and second, size-adjusted dose length product (DLP), we summarized dose and technical parameters and estimated which parameters contributed most to between-protocols dose variation. Lastly, we modeled the total population dose if all protocols with mean size-adjusted DLP above 433 or 645 mGy-cm were reduced to these thresholds. RESULTS: A total of 748,846 CTs were performed using 1033 unique protocols. When sorted by patient size, patients with larger abdominal diameters had increased dose and effective mAs (milliampere seconds), even after adjusting for patient size. When sorted by size-adjusted dose, patients in the highest versus the lowest decile in size-adjusted DLP received 6.4 times the average dose (1680 vs 265 mGy-cm) even though diameter was no different (312 vs 309 mm). Effective mAs was 2.1-fold higher, unadjusted CTDIvol 2.9-fold, and phase 2.5-fold for patients in the highest versus lowest size-adjusted DLP decile. There was virtually no change in kV (kilovolt). Automatic exposure control was widely used to modulate mAs, whereas kV modulation was rare. Phase was the strongest driver of between-protocols variation. Broad adoption of optimized protocols could result in total population dose reductions of 18.6-40%. CONCLUSION: There are large variations in radiation doses for routine abdomen CT unrelated to patient size. Modification of kV and single-phase scanning could result in substantial dose reduction. CLINICAL RELEVANCE: Radiation dose-optimization techniques for routine abdomen CT are routinely under-utilized leading to higher doses than needed. Greater modification of technical parameters and number of phases could result in substantial reduction in radiation exposure to patients. KEY POINTS: • Based on an analysis of 748,846 routine abdomen CT scans in adults, radiation doses varied tremendously across patients of the same size and optimization techniques were routinely under-utilized. • The difference in observed dose was due to variation in technical parameters and phase count. Automatic exposure control was commonly used to modify effective mAs, whereas kV was rarely adjusted for patient size. Routine abdomen CT should be performed using a single phase, yet multi-phase was common. • kV modulation by patient size and restriction to a single phase for routine abdomen indications could result in substantial reduction in radiation doses using well-established dose optimization approaches.

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.

OBJECTIVES: Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method. MATERIALS AND METHODS: Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists' visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson's correlation coefficients and receiver operating characteristics (ROC) analysis. RESULTS: SWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r = - 0.69 vs. r = - 0.49/r = - 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r = - 0.77 vs. r = - 0.69/r = - 0.65, p = 0.03/p = 0.002), and in the random sample (r = - 0.39 vs. r = - 0.26/r = - 0.25, p = 0.001/p = 0.007). CONCLUSION: The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists' visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists' visual scores. CLINICAL RELEVANCE STATEMENT: The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores. KEY POINTS: • A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images. • SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950). • SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.

Diagnostic performance and prognostic value of CT-defined visceral pleural invasion in early-stage lung adenocarcinomas.

OBJECTIVES: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. METHODS: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. RESULTS: In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03). CONCLUSION: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. CLINICAL RELEVANCE STATEMENT: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. KEY POINTS: • Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors.

Small arteriole sign: an imaging feature for staging T4a colon cancer.

OBJECTIVES: By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance. METHODS: Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test. RESULTS: In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001). CONCLUSION: Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer. CLINICAL RELEVANCE STATEMENT: Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer. KEY POINTS: • The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Acute and long-term renal effects after iodine contrast media-enhanced computerised tomography in the critically ill-a retrospective bi-centre cohort study.

OBJECTIVES: To determine if current clinical use of iodine contrast media (ICM) for computerised tomography (CT) increases the risk of acute kidney injury (AKI) and long-term decline in renal function in patients treated in intensive care. METHODS: A retrospective bi-centre cohort study was performed with critically ill subjects undergoing either ICM-enhanced or unenhanced CT. AKI was defined and staged based on the Kidney Disease Improve Global Outcome AKI criteria, using both creatinine and urine output criteria. Follow-up plasma creatinine was recorded three to six months after CT to assess any long-term effects of ICM on renal function. RESULTS: In total, 611 patients were included in the final analysis, median age was 65.0 years (48.0-73.0, quartile 1-quartile 3 (IQR)) and 62.5% were male. Renal replacement therapy was used post-CT in 12.9% and 180-day mortality was 31.2%. Plasma creatinine level on day of CT was 100.0 µmol/L (66.0-166.5, IQR) for non-ICM group and 77.0 µmol/L (59.0-109.0, IQR) for the ICM group. The adjusted odds ratio for developing AKI if the patient received ICM was 1.03 (95% confidence interval 0.64-1.66, p = 0.90). No significant association between ICM and increase in plasma creatinine at long-term follow-up was found, with an adjusted effect size of 2.92 (95% confidence interval - 6.52-12.36, p = 0.543). CONCLUSIONS: The results of this study do not indicate an increased risk of AKI or long-term decline in renal function when ICM is used for enhanced CT in patients treated at intensive care units. CLINICAL RELEVANCE STATEMENT: Patients treated in intensive care units had no increased risk of acute kidney injury or persistent decline in renal function after contrast-enhanced CT. This information underlines the need for a proper risk-reward assessment before denying patients a contrast-enhanced CT. KEY POINTS: • Iodine contrast media is considered a risk factor for the development of acute kidney injury. • Patients receiving iodine contrast media did not have an increased incidence of acute kidney injury or persistent decline in renal function. • A more clearly defined risk of iodine contrast media helps guide clinical decisions whether to perform contrast-enhanced CTs or not.

Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases.

OBJECTIVES: To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs). METHODS: A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets. RESULTS: Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images. CONCLUSION: The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs. CLINICAL RELEVANCE STATEMENT: GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent. KEY POINTS: • Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.

Semiquantitative index of symptomatic minor instability of the lateral elbow at CT arthrography (SMILE index): clinical applicability and reproducibility study.

OBJECTIVE: To assess the applicability of a semiquantitative index for symptomatic minor instability of the lateral elbow (SMILE). MATERIALS AND METHODS: CT arthrograms of consecutive patients with lateral elbow pain who underwent ultrasound-guided CT arthrography at our orthopedic center between April 2019 and May 2022 were included. Images were acquired at 100 kVp and 80 mAs. An expert radiologist (R1) and a radiology resident (R2) retrospectively performed an independent, blinded evaluation of the arthrograms to assess the presence of imaging findings suggestive of elbow instability. The SMILE index (0-8) was obtained adding (I) radial head chondromalacia (0 - 1); (II) humeral capitellum chondromalacia (0 - 1); (III) humeral trochlear ridge chondromalacia (0 - 1); (IV) annular ligament laxity (0 - 2); (V) synovial thickening (0 - 1); (VI) humeroradial joint asymmetry (0 - 1); and (VII) capsular tear (0 - 1). R1 repeated the assessment after 14 days. Cohen's weighted κ statistic and raw concordance were used to appraise reproducibility. RESULTS: Eighty patients (median age 49 years, interquartile range 40-53 years, 49, 61% males) underwent CT arthrography at our center, and 10 (12%) of them underwent bilateral elbow examination, leading to 90 included CT arthrograms. Median SMILE index was 4 (IQR: 2-5) for R1, 4 (IQR: 2-5) for R2, and 4 (IQR: 2-5) for the second assessment by R1. Intra-reader agreement was excellent (κ = 0.94, concordance 87%), while inter-reader agreement was substantial (κ = 0.75, concordance 67%). CONCLUSION: The proposed SMILE index showed good reproducibility; further studies are warranted to correlate our index with clinical and surgical data. CLINICAL RELEVANCE STATEMENT: Our scoring system allows a standardized evaluation of patients with lateral elbow pain and instability suitable for application into clinical practice, complementing the orthopedic surgeon's clinical diagnosis with imaging findings that may aid treatment choices. KEY POINTS: • Lateral elbow pain is often interpreted clinically as lateral epicondylitis, but it can also encompass intra-articular pathology. • The proposed arthrographic index allows comprehensive quantification of lateral elbow pathology with good reproducibility and application times. • Our index provides the orthopedic surgeon with information regarding intra-articular findings, aiding treatment choices.

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

OBJECTIVE: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). METHODS: A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24-28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. RESULTS: DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. CONCLUSION: For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. CLINICAL RELEVANCE STATEMENT: The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. KEY POINTS: • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

OBJECTIVES: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists. METHODS: A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard. RESULTS: The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced. CONCLUSIONS: The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation. CLINICAL RELEVANCE STATEMENT: This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care. KEY POINTS: • This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results.

OBJECTIVES: To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC). METHODS: A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)-derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson's correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. RESULTS: Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001). CONCLUSIONS: AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC. CLINICAL RELEVANCE STATEMENT: Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making. KEY POINTS: • Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)-derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer. • DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS. • Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.

N-(2,3-dihydroxypropyl) carbamoyl side chain: a potentially significant factor for recurrent iodinated contrast medium-related adverse drug reactions.

PURPOSE: To determine whether switching to contrast media based on the sharing of N-(2,3-dihydroxypropyl) carbamoyl side chain reduces the recurrence of iodinated contrast media (ICM)-associated adverse drug reactions (ADRs). MATERIALS AND METHODS: This single-center retrospective study included 2133 consecutive patients (mean age ± SD, 56.1 ± 11.4 years; male, 1052 [49.3%]) who had a history of ICM-associated ADRs and underwent contrast-enhanced CT examinations. The per-patient and per-exam-based recurrence ADR rates were compared between cases of switching and non-switching the ICM from ICMs that caused the previous ADRs, and between cases that used ICMs with common and different carbamoyl side chains from ICMs that caused the previous ADRs. Downgrade rates (no recurrence or the occurrence of ADR less severe than index ADRs) were also compared. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analysis were additionally performed. RESULTS: In per-patient analysis, switching of ICM showed a lower recurrence rate (switching, 10.4% [100/965] vs. non-switching, 28.4% [332/1168]), with the adjusted odds ratio (OR) of 0.27 (95% CI: 0.21, 0.34; p < 0.001). The result was consistent in PSM (OR, 0.29 [95% CI: 0.22, 0.39]; p < 0.001), IPTW (OR, 0.28 [95% CI: 0.22, 0.36]; p < 0.001), and in per-exam analysis (5.5% vs. 13.8%; OR, 0.32 [95% CI: 0.27, 0.37]; p < 0.001). There was lower per-exam recurrence (5.0% [195/3938] vs. 7.8% [79/1017]; OR, 0.63 [95% CI: 0.47, 0.83]; p = 0.001) and higher downgrade rates (95.6% [3764/3938] vs. 93.3% [949/1017]; OR, 1.51 [95% CI: 1.12, 2.03]; p = 0.006) when using different side chain groups. CONCLUSION: Switching to an ICM with a different carbamoyl side chain reduced the recurrent ADRs and their severity during subsequent examinations. CLINICAL RELEVANCE STATEMENT: Switching to an iodinated contrast media with a different carbamoyl side chain reduced the recurrent adverse drug reactions and their severity during subsequent examinations.

CT of appendicoliths in adult appendicitis: clinical significance and characteristics of overlooked cases.

OBJECTIVES: Accurate computed tomography (CT) identification of appendicoliths in adults with acute appendicitis is crucial as it may preclude nonoperative management due to high risk of failure and complications. This investigation aimed to identify the significance of appendicoliths in acute appendicitis and to evaluate the performance of portovenous-phase (PVP) CT and the consequences of overlooked appendicoliths. METHODS: CT examinations of 324 consecutive patients (mean age 51.9 years, 112 men) with pathologically confirmed acute appendicitis were retrospectively included. Two radiologists independently reviewed the images, and disagreement was resolved by a consensus. RESULTS: Appendicoliths were identified in 134/324 patients, of which 75 had complicated appendicitis. Among 190 patients without appendicoliths, 52 had complicated appendicitis. An appendicolith was independently associated with complicated appendicitis (adjusted odds ratio 2.289; 95% CI: 1.343-3.902; p = 0.002). The larger minimum diameter was significantly associated with complication. The 4.5-/6.0-mm cutoffs for minimum and maximum diameters of appendicoliths demonstrated 82.7%/85.3% sensitivity and 35.6%/33.9% specificity in predicting complications. The PVP alone had 82.1-88.1% sensitivity, respectively per patient and per appendicolith, and a 100% specificity in the detection of appendicoliths, as compared with combined noncontrast and PVP. PVP overlooked 28/237 appendicoliths (11.8%) corresponding to 24/134 patients (17.9%). Of the 24 patients with overlooked appendicoliths, 16 had complicated appendicitis but 14 were correctly categorized by findings other than appendicoliths. In total, 2/127 patients (1.6%) with complicated appendicitis were misdiagnosed as having uncomplicated appendicitis. CONCLUSIONS: Appendicoliths in acute appendicitis were strongly associated with complications. While PVP overlooked some appendicoliths, only 1.6% of complicated appendicitis were misclassified when considering other CT findings. CLINICAL RELEVANCE STATEMENT: This study found a strong association between appendicoliths and complications. Its presence may preclude conservative management. Although portovenous-phase CT overlooked some appendicoliths, the combination with other CT findings allowed correct classification in a vast majority of cases. KEY POINTS: • Accurate identification of appendicoliths is crucial for nonoperative management decisions in adult acute appendicitis. • Appendicoliths are strongly associated with complications in adult acute appendicitis. • Portovenous-phase CT overlooked some appendicoliths, but only a small percentage of patients with complicated appendicitis were misclassified when considering other CT findings.

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.