Confirmed and presumed canine insulinomas and their presumed metastases are most conspicuous in the late arterial phase in a triple arterial phase CT protocol.

Arterial enhancement is the commonly described characteristic of canine insulinomas in contrast-enhanced computed tomography (CECT). However, this finding is also reported as inconsistent. The main aim of this single-center retrospective observational study was to describe the contrast enhancement (CE) pattern of canine presumed and confirmed insulinomas and presumed metastases in three consecutive (early, mid, and late) arterial phases. Included dogs had a medical-record-based clinical or cytological/histopathological diagnosis of insulinoma and quadruple-phase CECT. The arterial phases were identified according to published literature. The arterial enhancement of confirmed and presumed lesions was assessed using a visual grading score. Twelve dogs with a total of 17 pancreatic nodules were analyzed. Three dogs had multiple pancreatic nodules and nine had solitary findings. Four insulinomas were histopathologically confirmed. Late arterial phase (LAP) images demonstrated the largest number of pancreatic nodules reaching the highest enhancement scores (n = 13, 76%). All analyzed dogs had CT evidence of arterially enhancing nodules in the liver (n = 12), seven in the hepatic, splenic, or colic lymph nodes, and three in the spleen. Three out of five sampled livers and three lymph nodes were metastatic. All sampled spleens were benign. Avid arterial enhancement was the most dominant feature of canine presumed and confirmed insulinomas and presumed metastases in quadruple-phase CECT. The highest enhancement scores were observed primarily in LAP, followed by MAP. Authors, therefore, recommend including LAP in the standard CT protocol for dogs with suspected pancreatic insulinomas.

Enhancement pattern of differentiated and undifferentiated gastric carcinoma on multiphasic contrast-enhanced computed tomography.

PURPOSE: To study the enhancement pattern of differentiated and undifferentiated gastric carcinoma on multiphasic contrast-enhanced computed tomography (CT). MATERIAL AND METHODS: Seventy patients with biopsy-proven gastric cancer underwent multiphasic contrast-enhanced CT. The CT protocol include plain, arterial, portal venous, and hepatic venous phase. Tumour size, location, peak-enhancement characteristics, and staging were evaluated. RESULTS: The peak-enhancement type was 'arterial' in 20 out of 28 within the differentiated-type GCAs and 'portalvenous' in 37 out of 42 within the undifferentiated-type GCAs (c2 statistic with Yates correction = 23.3981, p < 0.00001). The maximum attenuation value was statistically significant for the arterial phase between differentiated and undifferentiated GCAs (p < 0.05). CONCLUSIONS: Assessing peak-enhancement in a multiphasic CT can help identify the histological subcategory of gastric carcinomas that has prognostic significance. Arterial phase peak-enhancement is frequently seen in differentiated carcinomas whereas venous phase peak-enhancement is seen in undifferentiated carcinomas.

Synchronous tumours detected during cancer patient staging: prevalence and patterns of occurrence in multidetector computed tomography.

PURPOSE: The incidental detection of one or more additional primary tumours during computed tomography (CT) staging of a patient with known malignancy is rare but possible. This occurrence should be considered by the radiologist when a new lesion is detected, especially if the lesion location is atypical for metastases. The purpose of this report was to document the usefulness of total body CT scan to detect synchronous primary malignancies in cancer patients undergoing a staging workup. MATERIAL AND METHODS: This was done by reviewing the staging CT studies of the adult patients with a newly diagnosed cancer evaluated during a five-year period in a single cancer institute in order to identify any possible correlation, establishing which tumours are more frequently combined with a second tumour and which second tumours are more commonly present. RESULTS: Among the patients with a second tumour, the most frequent first primary tumours were melanoma (eight patients, 17.8%), lymphoma (seven patients, 15.6%), and prostate carcinoma (seven patients, 15.6%). The most frequent incidentally detected second tumours were hepatocellular carcinoma (nine patients, 20% of 45 incidental tumours), renal carcinoma (eight patients, 17.8%), lung carcinoma (seven patients, 15.6%), and bladder carcinoma (four patients, 8.9%). One patient had three primary tumours synchronously. CONCLUSIONS: We believe that the radiologist's knowledge of the prevalence and pattern of occurrence of these multiple primary malignancies represents added diagnostic value.

Using 80 kVp on a 320-row scanner for hepatic multiphasic CT reduces the contrast dose by 50 % in patients at risk for contrast-induced nephropathy.

OBJECTIVES: We evaluated the effects of a low contrast material (CM) dose protocol using 80-kVp on the image quality of hepatic multiphasic CT scans acquired on a 320-row CT scanner. METHODS: We scanned 30 patients with renal insufficiency (eGFR < 45 mL/min/1.73 m2) using 80-kVp and a CM dose of 300mgI/kg. Another 30 patients without renal insufficiency (eGFR > 60 mL/min/1.73 m2) were scanned with the conventional 120-kVp protocol and the standard CM dose of 600mgI/kg. Quantitative image quality parameters, i.e. CT attenuation, image noise, and the contrast-to-noise ratio (CNR) were compared and the visual image quality was scored on a four-point scale. The volume CT dose index (CTDIvol) and the size-specific dose estimate (SSDE) recorded with the 80- and the 120-kVp protocols were also compared. RESULTS: Image noise and contrast enhancement were equivalent for the two protocols. There was no significant difference in the CNR of all anatomic sites and in the visual scores for overall image quality. The CTDIvol and SSDE were approximately 25-30 % lower under the 80-kVp protocol. CONCLUSION: Hepatic multiphase CT using 80-kVp on a 320-row CT scanner allowed for a decrease in the CM dose and a reduction in the radiation dose without image quality degradation in patients with renal insufficiency. KEY POINTS: • The 80-kVp CT protocol enabled reduction of contrast dose by 50 % • The 80-kVp CT protocol reduced the radiation dose by 25-33 % • There was no degradation in the image quality of the 80-kVp protocol.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.

Objective: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. Results: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). Conclusion: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

An investigation on gastric cancer staging using CT structured report.

OBJECTIVE: To evaluate the clinical performance of structured report (SR) for CT in patients with pre-operative staging of gastric cancer, compared to non-SR. METHODS: Retrospectively, 51 consecutive cases with primary gastric cancer staging were enrolled. Every SR or non-SR was performed by two GI radiologists (1 junior and 1 senior). Interobserver agreement was conducted between the junior and senior groups for both SR and non-SR. 10 key features required for lesion description and staging were assessed between SR and non-SR. Diagnostic content between SR and non-SR was also compared. Accuracy of SR and non-SR on T staging was measured. Subjective evaluation of SR vs. non-SR was also conducted in form of survey by 20 radiologists and 3 GI surgeons. RESULTS: Interobserver agreement showed excellent in SR (Kappa = 1, P < 0.001), but poor in non-SR (Kappa = 0.036, P = 0.455). For the 10 key features required for lesion assessment, non-SR showed 6.84 ±â€¯0.83 while SR reported all of them (P < 0.001). Statistically significant improvement was observed in the SR for parts of key features, especially for assessment of adjacent organs and vessels (P < 0.001). Accuracy comparison of T staging showed higher in SR for cohort of T4a (P = 0.028<0.05). The scores of subjective evaluation were higher (P < 0.05) in SR than in non-SR by both radiologists and surgeons. Meanwhile, the inter-observer agreement among surgeons was good in SR with significance (w=0.53, P = 0.005 for efficiency; w=0.638, P < 0.001 for integrity) but poor in non-SR. CONCLUSIONS: SR of gastric multiphasic CT ensured reliable detection of all relevant key features for staging along with reproducible documentation, which was not always the case for non-SR. In addition, SR has the potential in improving diagnostical accuracy of T staging and was welcomed by clinicians.

Association of tumor grade, enhancement on multiphasic CT and microvessel density in patients with clear cell renal cell carcinoma.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) comprises nearly 90% of all diagnosed RCC subtypes and has the worst prognosis and highest metastatic potential. The strongest prognostic factors for patients with ccRCC include histological subtype and Fuhrman grade, which are incorporated into prognostic models. Since ccRCC is a highly vascularized tumor, there may be differences in enhancement patterns on multidetector CT (MDCT) due to the hemodynamics and microvessel density (MVD) of the lesions. This may provide a noninvasive method to characterize incidentally detected low- and high-grade ccRCCs on MDCT. The purpose of our study was to determine the correlation between MDCT enhancement parameters, ccRCC MVD, and Fuhrman grade to determine its utility and value in assessing tumor vascularity and grade in vivo. METHODS: In this retrospective, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low-grade (LG), and 43 high-grade (HG) ccRCCs underwent preoperative four-phase MDCT. A 3D volume of interest (VOI) was obtained for every tumor and absolute enhancement and the wash-in/wash-out of enhancement for each phase was assessed. Immunohistochemistry on resected specimens was used to quantify MVD. Linear regression and Pearson correlation were used to investigate the strength of the association between 3D VOI enhancement and MVD. Stepwise logistic regression analysis determined independent predictors of HG ccRCC. Cut-off values and odds Ratio (OR) with 95% CIs were reported. The clinical, radiomic, and pathologic features with the highest performance in the stepwise logistic regression analysis were evaluated using receiver operator characteristics (ROC) and area under the curve (AUC). RESULTS: Absolute enhancement in the nephrographic phase < 52.1 Hounsfield Units (HU) (HR 0.979, 95% CI 0.964-0.994, p value = 0.006), lesion size > 4.3 cm (HR 1.450, 95% CI 1.211-1.738, p value < 0.001), and an intratumoral MVD < 15% (HR 0.932, 95% CI 0.867-1.002, p value = 0.058) were independent predictors of HG ccRCC with an AUC of 0.818 (95% CI 0.725-0.911). HG ccRCCs had a significant association between 3D VOI enhancement and MVD in each post-contrast phase (r2 = 0.238 to 0.455, p < 0.05). CONCLUSIONS: Absolute enhancement of the entire lesion obtained from a 3D VOI in the nephrographic phase on preoperative MDCT can provide quantitative data that are a significant, independent predictor of a high-grade clear cell RCC and can be used to assess tumor vascularity and grade in vivo.

Liver metastases of neuroendocrine tumors: is it possible to diagnose different histologic subtypes depending on multiphasic CT features?

PURPOSE: To assess and compare the multiphasic computed tomography (CT) features of neuroendocrine tumor (NET) liver metastases and to investigate the possibility to predict the histologic subtype of the primary tumor. MATERIALS AND METHODS: Between January 2013 and December 2017 patients with biopsy proven NET with at least one liver metastasis who underwent multiphasic CT were enrolled in this study. All cases were acquired using a standardized multiphasic liver CT protocol, arterial, portal, and hepatic venous phases were obtained. Images were retrospectively analyzed in consensus by two abdominal radiologists blinded to clinical data and histologic subtype. The size, number, and location of lesions were noted. Enhancement patterns of each lesion on arterial, portal, and hepatic venous phases were assessed. For quantitative analysis, CT attenuation of tumors, liver parenchyma, and aorta were measured using a circular region of interest (ROI) on arterial, portal, and hepatic venous phases for reflecting the blood supply of the tumor. Tumor-to-aorta and tumor-to-liver ratio were calculated in all three phases. Differences between subtypes of NET liver metastases were studied using ROC analysis of clustered data. RESULTS: A total of 255 neuroendocrine tumor liver metastases divided into 101 (39.6%) pancreatic, 60 (23.5%) gastroenteric and 94 (36.8%) lung NET liver metastases were analyzed. Contrast enhancement of lesions was homogeneous in 78% of patients (n = 199), which was significantly more frequent in patients with pancreatic group than in those with gastroenteric origin (n = 90, 89.1% vs. n = 28, 46.7%; p < 0.001). Gastroenteric NET metastases frequently showed heterogeneous enhancement, which was significantly higher than in the other two groups (50% vs. 3% and 2%). With respect to the location of the primary tumor, the difference in enhancement patterns of the liver lesions was statistically significant (p < 0.001). Pancreatic NET metastases were mostly hyperdense on arterial images and isodense on portal and hepatic venous phase images (79.2%, n = 80). Gastroenteric NET metastases were mostly hyperdense on arterial phase images and hypodense on portal and hepatic venous phase images (n = 28, 46.7%). The most frequent pattern for lung NET metastases was hypoattenuation on all three phase images (n = 44, 46.8%). ROC analysis of clustered data revealed statistically significant differences between pancreatic NET liver metastases, gastroenteric NET liver metastases, and lung NET liver metastases in terms of tumor-to-aorta (T-A) ratio and tumor-to-liver (T-L) ratio (p < 0.001). CONCLUSION: We observed statistically significant differences in multiphasic CT features (enhancement pattern, T-A ratio, and T-L ratio) between histologic subtypes of NET liver metastases. As the difference in histological subtypes of NET liver metastases results in a different prognosis and different management strategy, these CT features might help to identify the primary tumor when it is not known to ensure accurate tumor staging and to provide optimal treatment.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.

PURPOSE: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. METHODS: With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. RESULTS: We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. CONCLUSIONS: The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

Evaluating the Sensitivity of Arterial Phase CT Images for Detection of Hepatic GIST Metastases.

Gastrointestinal stromal tumor (GIST) frequently metastasizes to the liver, and conventional staging computed tomography (CT) protocols use multiphasic contrast enhancement for detection of hepatic lesions. We evaluated the sensitivity of arterial phase CT imaging for hepatic GIST metastases compared with that of standard (portal venous [PV]) phase imaging. We conducted a retrospective review of patients who presented with hepatic GIST metastases identified on staging CT examinations between 2005 and 2015. Arterial and PV phase CT images were randomized and reviewed by 2 radiologists blinded to clinical history, correlative imaging, and number of controls. In total, 32 patients had hepatic metastases identified on multiphasic (arterial and PV) staging CT examinations. There was no significant difference in identification of metastases between arterial and PV phase imaging (31 vs 32, P = .32). Lesion size measurements did not significantly differ (P = .58). Arterial phase CT imaging did not significantly increase the sensitivity for hepatic GIST metastases compared with PV phase imaging alone.

Utility of dual phase liver CT for metastatic melanoma staging and surveillance.

RATIONALE AND OBJECTIVES: To evaluate the clinical utility of dual phase computed tomography (CT) for assessment of hepatic metastases in patients with metastatic melanoma. MATERIALS AND METHODS: A retrospective case-control study of dual phase CT examinations consisting of late hepatic arterial and portal venous phases performed on patients with melanoma was undertaken. In 2010, 420 dual phase CT examinations were performed on 188 patients. Of these, 46 CT examinations on 24 patients with hepatic metastases were combined with 52 control studies for evaluation. Two blinded reviewers independently evaluated single portal venous phase alone and dual phase imaging on separate occasions. The presence of hepatic lesions, the conspicuity of the lesions, and the likelihood that the detected lesions were metastases was recorded. Agreement between readers, sensitivity and specificity was calculated. RESULTS: In no case was hepatic metastatic disease only apparent on arterial phase imaging. Arterially enhancing hepatic lesions only visible on the arterial phase or much more conspicuous on the arterial phase were present in 10 studies (10%), all of which were benign. Liver metastases were rated as being more accurately assessed on the portal venous phase in up to 100%. In a per scan analysis dual phase and venous phase imaging had similar sensitivities of 96% (95%, CI: 86-100) and 98% (95%, CI: 89-100), respectively. CONCLUSION: Single portal venous phase imaging is adequate for staging and surveillance in patients with metastatic melanoma.