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Background: To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC)., Methods: Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors' 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA)., Results: There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model., Conclusions: Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC., (© 2024. The Author(s).)

Aims: The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma., Materials and Methods: The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models., Results: The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set., Conclusions: In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: To evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa)., Materials and Methods: A retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA)., Results: The normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively., Conclusion: The proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa., (© 2024. The Author(s).)

Background: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma., Methods: This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method., Results: The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively., Conclusions: A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma., (© 2024. The Author(s).)

Objectives: To develop radiomics-based classifiers for preoperative prediction of fibrous capsule invasion in renal cell carcinoma (RCC) patients by CT images., Methods: In this study, clear cell RCC (ccRCC) patients who underwent both preoperative abdominal contrast-enhanced CT and nephrectomy surgery at our hospital were analysed. By transfer learning, we used base model obtained from Kidney Tumour Segmentation challenge dataset to semi-automatically segment kidney and tumours from corticomedullary phase (CMP) CT images. Dice similarity coefficient (DSC) was measured to evaluate the performance of segmentation models. Ten machine learning classifiers were compared in our study. Performance of the models was assessed by their accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC). The reporting and methodological quality of our study was assessed by the CLEAR checklist and METRICS score., Results: This retrospective study enrolled 163 ccRCC patients. The semiautomatic segmentation model using CMP CT images obtained DSCs of 0.98 in the training cohort and 0.96 in the test cohort for kidney segmentation, and DSCs of 0.94 and 0.86 for tumour segmentation in the training and test set, respectively. For preoperative prediction of renal capsule invasion, the AdaBoost had the best performance in batch 1, with accuracy, precision, recall, and F1-score equal to 0.8571, 0.8333, 0.9091, and 0.8696, respectively; and the same classifier was also the most suitable for this classification in batch 2. The AUCs of AdaBoost for batch 1 and batch 2 were 0.83 (95% CI: 0.68-0.98) and 0.74 (95% CI: 0.51-0.97), respectively. Nine common significant features for classification were found from 2 independent batch datasets, including morphological and texture features., Conclusions: The CT-based radiomics classifiers performed well for the preoperative prediction of fibrous capsule invasion in ccRCC., Advances in Knowledge: Noninvasive prediction of renal fibrous capsule invasion in RCC is rather difficult by abdominal CT images before surgery. A machine learning classifier integrated with radiomics features shows a promising potential to assist surgical treatment options for RCC patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer., Methods: Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K (HU) ), effective atomic number (Z eff ), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test., Results: The preoperative spectral CT parameters (40-70 keV attenuation, K (HU) , Z eff , and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%., Conclusion: Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: To evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detecting gastric cancer, and determining the depth of invasion., Methods: Forty-six patients with resected gastric cancer who underwent preoperative contrast-enhanced U-HRCT were included. The image quality of U-HRCT reconstructed using three different methods (standard DLR [AiCE], improved DLR-AiCE-Body Sharp [improved AiCE-BS], and hybrid-IR [AIDR3D]) was compared. Visualization of the gastric wall's three-layered structure in four regions and the visibility of gastric cancers were compared between U-HRCT and conventional HRCT (C-HRCT). The diagnostic ability of U-HRCT with the improved AiCE-BS for determining the depth of invasion of gastric cancers was assessed using postoperative pathology specimens., Results: The mean noise level of U-HRCT with the improved AiCE-BS was significantly lower than that of the other two methods (p < 0.001). The overall image quality scores of the improved AiCE-BS images were significantly higher (p < 0.001). U-HRCT demonstrated significantly better conspicuity scores for the three-layered structure of the gastric wall than C-HRCT in all regions (p < 0.001). In addition, U-HRCT was found to have superior visibility of gastric cancer in comparison to C-HRCT (p < 0.001). The correct diagnostic rates for determining the depth of invasion of gastric cancer using C-HRCT and U-HRCT were 80%., Conclusions: U-HRCT reconstructed with the improved AiCE-BS provides clearer visualization of the three-layered gastric wall structure than other reconstruction methods. It is also valuable for detecting gastric cancer and assessing the depth of invasion., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: In pancreatic adenocarcinoma, the difficult distinction between normal and affected pancreas on CT studies may lead to discordance between the pre-surgical assessment of vessel involvement and intraoperative findings. We hypothesize that a visual aid tool could improve the performance of radiology residents when detecting vascular invasion in pancreatic adenocarcinoma patients. Methods: This study consisted of 94 pancreatic adenocarcinoma patient CTs. The visual aid compared the estimated body fat density of each patient with the densities surrounding the superior mesenteric artery and mapped them onto the CT scan. Four radiology residents annotated the locations of perceived vascular invasion on each scan with the visual aid overlaid on alternating scans. Using 3 expert radiologists as the reference standard, we quantified the area under the receiver operating characteristic curve to determine the performance of the tool. We then used sensitivity, specificity, balanced accuracy ((sensitivity + specificity)/2), and spatial metrics to determine the performance of the residents with and without the tool. Results: The mean area under the curve was 0.80. Radiology residents' sensitivity/specificity/balanced accuracy for predicting vascular invasion were 50%/85%/68% without the tool and 81%/79%/80% with it compared to expert radiologists, and 58%/85%/72% without the tool and 78%/77%/77% with it compared to the surgical pathology. The tool was not found to impact the spatial metrics calculated on the resident annotations of vascular invasion. Conclusion: The improvements provided by the visual aid were predominantly reflected by increased sensitivity and accuracy, indicating the potential of this tool as a learning aid for trainees., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Rationale and Objectives: To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs)., Materials and Methods: Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test., Results: Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05)., Conclusion: A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Chenggong Yan reports financial support was provided by National Natural Science Foundation of China (grant no.82271987). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT)., Materials and Methods: For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant., Results: Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists., Conclusion: Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma., (© 2024. The Author(s).)

Purpose: To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance., Materials and Methods: We divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification., Results: The CNN model's diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses., Conclusions: This CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies., (© 2024. The Author(s).)

Objectives: The aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa)., Methods: A retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram's performance was assessed based on discrimination, calibration, and clinical utility., Results: After obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742-0.880), 0.845 (95% CI, 0.781-0.908), and 0.896 (95% CI, 0.846-0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703-0.913), 0.847 (95% CI, 0.739-0.954), and 0.887 (95% CI, 0.803-0.971). According to the DeLong test, the radiomics-clinical nomogram's AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models., Conclusions: CT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa., (© 2024. The Author(s).)

Background: At present, lobectomy is still the standard treatment for lung cancer. Judging whether a lesion is invasive adenocarcinoma (IA) has important guiding significance for determining the scope of surgical resection. The commonly used methods are intraoperative frozen sections and computed tomography (CT) signs. There is still controversy about the accuracy of both in judging the invasiveness of ground-glass nodules (GGNs)., Methods: The clinical data of patients with GGNs who underwent surgery were collected. According to the results of univariate analysis, the variables with statistical differences were selected and included in logistic regression multivariate analysis. The predictive variables were determined and the receiver operating characteristic (ROC) curve was drawn in order to achieve the area under the curve (AUC) value., Results: According to the results of logistic regression analysis, the longest diameter and maximum CT value of nodules were independent risk factors for IA. The mathematical prediction model of CT signs was determined, and the ROC curves of CT signs and intraoperative frozen sections (FS) were drawn, respectively. The AUC values under the curves were calculated to be 0.873 and 0.807, respectively. The mathematical prediction model of intraoperative frozen section combined with CT signs was established. A ROC curve was drawn and the AUC was calculated to be 0.925., Conclusions: The diagnostic accuracy of CT signs in judging whether nonbenign GGNs were IA was higher than that of intraoperative FS. Combined with CT signs and intraoperative FS to establish a mathematical prediction model, the diagnostic accuracy of judging whether nonbenign GGNs are IA is significantly improved., (© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Objective: To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs., Methods: We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison., Results: The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs ( p 's value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features., Conclusions: Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs., Advances in Knowledge: The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

Aim: To investigate the value of dual-energy spectral computed tomography (DESCT) for evaluating the histological subtypes of solid-dominant invasive lung adenocarcinoma (SILADC)., Materials and Methods: Sixty-seven patients with SILADC were enrolled. All patients underwent DESCT and were divided into Group I (those with a lepidic/acinar/papillary predominant pattern) and Group II (those with a solid/micropapillary predominant pattern) based on their correlation with prognosis. Patient clinicopathological characteristics, DESCT morphological features, and quantitative parameters of the tumours were compared between both groups. Multiparametric analysis was performed using binary logistic regression with DESCT findings. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analysis., Results: Patient gender, lymph nodes status, pathological TNM stage, and histological differentiation significantly differed between the two groups (all p<0.05). Moreover, significant differences were observed between both groups in DESCT morphological features including tumour size, necrosis, calcification, air bronchogram, and vascular convergence sign, and quantitative parameters including K 40-65 keV , effective atomic number, and water concentration on unenhanced CT and iodine concentration in the arterial and venous phases (all p<0.05). Multiparametric analysis showed that tumour size, air bronchogram, K 40-65 keV and effective atomic number on unenhanced CT were the most effective variations for predicting the histological subtypes of SILADC and obtained an area under the ROC curve (AUC) of 0.906., Conclusions: DESCT was useful for differentiating histological subtypes with different prognosis of SILADC., (Copyright © 2020 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study ( https://ClinicalTrials.gov , NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training ( n = 229) and test ( n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. Online supplemental material is available for this article. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm 2 ; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs ( p < 0.001), whereas the latter three were similar in size ( p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs ( p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm 2 ), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

OBJECTIVE. The purpose of this study is to compare the CT features of colloid carcinoma and tubular adenocarcinoma of the pancreas arising in association with intraductal papillary mucinous neoplasms (IPMNs). MATERIALS AND METHODS. The preoperative CT images of 85 patients with histopathologically proven IPMNs and associated invasive adenocarcinoma located next to each other were retrospectively reviewed. Twenty-nine patients (34.1%; 19 men and 10 women; mean [± SD] age, 68.0 ± 9.5 years) had invasive colloid carcinoma, and 56 patients (65.9%; 31 men and 25 women; mean age, 70.8 ± 10.6 years) had invasive tubular adenocarcinoma. We compared the following CT features between the two groups: IPMN type, main pancreatic duct (MPD) and common bile duct diameters, diameter and characteristics of the largest cystic lesion for branch duct and mixed-type IPMNs, presence of an extracystic or extraductal solid mass next to the cystic lesion or MPD, morphologic features of the upstream MPD in relation to the cystic lesion or solid mass, and presence of a fistula to the adjacent organs. RESULTS. An MPD size of 9.5 mm or greater, a largest cystic lesion diameter of 28 mm or greater, location in the head or neck, septation, calcification, presence of a mural nodule(s) within a cystic lesion or MPD, and presence of a fistula were all more commonly associated with colloid carcinoma. In contrast, presence of an extracystic or extraductal solid mass and an abrupt change in the caliber of the dilated MPD were associated with tubular adenocarcinoma. The best CT feature for differentiating between the two groups was the morphologic features of the upstream MPD in relation to the cystic lesion or solid mass (sensitivity, 81.3%; specificity, 92.3%). CONCLUSION. Preoperative CT is helpful in differentiating two types of invasive carcinoma arising in association with IPMNs. These findings are clinically important because prognosis is better for colloid carcinoma than for tubular adenocarcinoma.

Objectives: Lung adenocarcinomas which manifest as ground-glass nodules (GGNs) have different degrees of pathological invasion and differentiating among them is critical for treatment. Our goal was to evaluate the addition of marginal features to a baseline radiomics model on computed tomography (CT) images to predict the degree of pathologic invasiveness., Methods: We identified 236 patients from two cohorts (training, n = 189; validation, n = 47) who underwent surgery for GGNs. All GGNs were pathologically confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The regions of interest were semi-automatically annotated and 40 radiomics features were computed. We selected features using L1-norm regularization to build the baseline radiomics model. Additional marginal features were developed using the cumulative distribution function (CDF) of intratumoral intensities. An improved model was built combining the baseline model with CDF features. Three classifiers were tested for both models., Results: The baseline radiomics model included five features and resulted in an average area under the curve (AUC) of 0.8419 (training) and 0.9142 (validation) for the three classifiers. The second model, with the additional marginal features, resulted in AUCs of 0.8560 (training) and 0.9581 (validation). All three classifiers performed better with the added features. The support vector machine showed the most performance improvement (AUC improvement = 0.0790) and the best performance was achieved by the logistic classifier (validation AUC = 0.9825)., Conclusion: Our novel marginal features, when combined with a baseline radiomics model, can help differentiate IA from AIS and MIA on preoperative CT scans., Key Points: • Our novel marginal features could improve the existing radiomics model to predict the degree of pathologic invasiveness in lung adenocarcinoma.

A 73-year-old woman was diagnosed by bronchoscopic examination with primary left lung cancer (Adenocarcinoma, cT3N0M0, stage IIB), which was closely adjacent to the descending aorta in contrast enhanced computed tomography (CT). This CT did not reveal any invasion of a tumor into the descending aorta, and a dynamic fourth dimension CT (4D-CT) indicated that there was no invasion of the aorta by this tumor, so we decided to perform surgery. The operative procedure was a left lower lobectomy and lymph node dissection with the use of a thoracoscope. An intraoperative finding was that the tumor had not invaded the aorta. There are few reports about the evaluation of vascular invasion using the dynamic 4D-CT. We consider that the dynamic 4D-CT gave very useful information about vascular invasion.

Computed tomography (CT) is widely used in the pretreatment period of early gastric cancer (EGC). Only few studies have reported low accuracy of CT imaging for T and N staging in patients with EGC. However, owing to the limited number of studies, the value of CT imaging for EGC staging is not well known. Thus, we conducted a retrospective cross-sectional study regarding the associations among submucosal invasion, lymph node metastasis, and CT findings.The medical records of patients with EGC who had surgery or endoscopic resection were reviewed in a single center from January 2011 to December 2016. We evaluated the histological type, invasion depth, and lymph node (LN) metastasis on the basis of two-dimensional CT findings.We enrolled 1544 patients. Submucosal (SM) invasion was related to tumor size, histological type, and wall thickening or enhancement on CT images. Deep SM invasion (>500 μm) was also related to tumor size, poorly differentiated type, and abnormal CT findings (wall thickening, enhancement, and central depression). Among the patients with LN reactive positivity (0.5-1 cm), those who were female and had a tumor invasion of >1000 μm showed a higher prevalence of LN metastasis. The false-negative LN group had a higher prevalence of large tumors (>3 cm), poor differentiation, and SM invasion than the true-negative group.Wall thickening, enhancement, and central depression on CT images might be related to SM invasion. Patients with any positive CT findings needs more attention when performing ESD.

Importance: Preoperative TNM stratification of colon cancer on computed tomography (CT) does not identify patients who are at high risk of recurrence that could be selected for preoperative treatment., Objective: To evaluate the utility of CT findings for prognosis of sigmoid colon cancer., Design, Setting, and Participants: This prognostic study used retrospective data from patients who underwent bowel resection for sigmoid colon cancer between January 1, 2006, and January 1, 2015, at a tertiary care center receiving international and national referrals for colorectal cancer. Statistical analysis was performed in April 2019., Main Outcomes and Measures: Cox proportional hazards regression analysis was performed to investigate CT findings associated with disease recurrence. Kaplan-Meier survival plots were calculated for disease-free survival using CT staging systems., Results: Of the 414 patients who had sigmoid colon cancer (248 [60.0%] men; mean [SD] age, 66.1 [12.7] years), with median follow-up of 61 months (interquartile range, 40-87 months), 122 patients (29.5%) developed disease recurrence. On multivariate analysis, nodal disease was not associated with disease recurrence; only tumor deposits (hazard ratio [HR], 1.90; 95% CI, 1.21-2.98; P = .006) and extramural venous invasion (HR, 1.97; 95% CI, 1.26-3.06; P = .003) on CT were associated with disease recurrence. Significant differences in disease-free survival were found using CT-T3 substage classification (HR, 1.88; 95% CI, 1.32-2.68) but not CT-TNM (HR, 1.55; 95% CI, 0.94-2.55). The presence of tumor deposits or extramural venous invasion on CT (HR, 2.45; 95% CI, 1.68-3.56) had the strongest association with poor outcome., Conclusions and Relevance: In this study, T3 substaging and detection of tumor deposits or extramural venous invasion on preoperative CT scans of sigmoid colon cancer were prognostic factors for disease-free survival, whereas TNM and nodal staging on CT had no prognostic value. T3 substaging and detection of tumor deposits or extramural venous invasion of sigmoid colon cancer was superior to TNM on CT and could be used to preoperatively identify patients at high risk of recurrence.

Aim: This study evaluated the relationship between three-dimensional (3D) mean computed tomography (CT) attenuation values of ground-glass nodules (GGN) and pathological invasiveness in early lung adenocarcinoma. The diagnostic accuracy of 3D CT attenuation values was compared with that of two-dimensional (2D) CT attenuation values and standardised uptake value on positron-emission tomography (PET)., Materials and Methods: Surgical and radiological data from 96 pure or part-solid GGNs of <20 mm were analysed retrospectively. Mean 2D and 3D CT attenuation values of the tumours were obtained with semi-automated volumetric software. Pathological invasiveness was diagnosed according to the International Association for the Study of Lung Cancer (IASLC))/American Thoracic Society (ATS)/European Respiratory Society (ERS) classification. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. Univariate and multivariate analyses determined relationships between pathological invasiveness and clinical/radiological findings. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off value for detecting invasive adenocarcinoma., Results: A total of 66 non-invasive and 30 invasive adenocarcinoma cases between 2010 and 2016 were analysed. Univariate analysis revealed four tumour invasiveness-associated predictors: maximum diameter, SUVmax, mean 2D CT attenuation value, and mean 3D CT attenuation value (p<0.05). Multivariate analysis revealed that the maximum diameter, SUVmax, and mean 3D CT attenuation value were significant predictors of pathological invasiveness (p=0.023, 0.022, 0.004). The area under the ROC curve to predict invasive adenocarcinoma for mean 3D CT attenuation value was 0.838 and the cut-off value was -489 HU., Conclusion: The mean 3D CT attenuation value could distinguish pre-invasive lesions and minimally invasive adenocarcinoma from invasive adenocarcinoma., (Copyright © 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Purpose: The aim of this study is to evaluate the diagnostic contribution of diffusion-weighted magnetic resonance imaging (MRI) and computed tomography (CT) to distinguish extramural venous invasion (EMVI) in rectal adenocarcinoma., Materials and Methods: Fifty-eight patients who had been diagnosed with rectal adenocarcinoma (30 patients with EMVI and 28 patients without EMVI) were enrolled in the study. Apparent diffusion coefficient (ADC) values of the tumour and the EMVI (+) vein, the lengths of the tumours were measured on MRI. The diameters of the superior rectal vein (SRV)-inferior mesenteric vein (IMV) and distant metastatic spread were evaluated on CT. The ability of these findings to detect EMVI was assessed using receiver operating characteristic (ROC) analysis. Pathology was accepted as the reference test for EMVI., Results: Mean diameters of the SRV (4.9 ± 0.9 mm vs 3.7 ± 0.8 mm) and IMV (6.9 ± 0.8 mm vs 5.4 ± 0.9 mm) were significantly larger (P < .001) and tumour ADC values were significantly lower (0.926 ± 0.281 × 10 -3  mm 2 /s vs 1.026 ± 0.246 × 10 -3  mm 2 /s; P = .032) in EMVI (+) patients. Diameters of 3.95 mm for the SRV (area under the curve [AUC] ± standard error [SE]: 0.851 ± 0.051, P < .001, sensitivity: 93.3%, specificity: 67.9%) and 5.95 mm for the IMV (AUC ± SE: 0.893 ± 0.040, P < .001, sensitivity: 93.3%, specificity: 71.4%) and an ADC value of 0.929 × 10 -3  mm 2 /s (AUC ± SE: 0.664 ± 0.072, P = .032 sensitivity: 76.7%, specificity: 57.1%) were found to be cutoff values, determined by ROC analysis, for detection of EMVI. Distant metastases were significantly more prevalent in EMVI (+) patients (P < .001)., Conclusion: The measurement of ADC values and SRV-IMV diameters seems to have contribution for diagnosis of EMVI in rectal adenocarcinoma. EMVI (+) patients appear to have higher risks of distant metastases at diagnosis., (Copyright © 2019 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.)

OBJECTIVE. The purpose of this study is to evaluate high-resolution CT (HRCT) combined with PET/CT for preoperative differentiation of invasive adenocarcinoma (IAC) from preinvasive lesions and minimally invasive adenocarcinoma (MIA) (the combination of which is hereafter referred to as preinvasive-MIA) in lung adenocarcinoma manifesting as ground-glass opacity nodules (GGNs) 3 cm or smaller. MATERIALS AND METHODS. We retrospectively analyzed the data of patients with lung adenocarcinoma with GGNs that were 3 cm or smaller between November 2011 and November 2018. The HRCT and PET/CT parameters for GGNs were compared to differentiate between IAC and preinvasive-MIA. Qualitative and quantitative parameters were analyzed using univariate and multivariate logistic regression models. The diagnostic performance of different parameters was compared using ROC curves and the McNemar test. RESULTS. The study enrolled 89 patients (24 men and 65 women) with lung adenocarcinoma who had a mean (± SD) age of 60.1 ± 8.1 years (range, 36-78 years). The proportions of mixed GGN type, polygonal or irregular shape, lobulated or spiculated edge, and dilated, distorted, or cutoff bronchial sign were higher for IAC GGNs than for preinvasive-MIA GGNs, and the attenuation value of the ground-glass opacity component on CT (CT GGO ), maximum standardized uptake value, and the standardized uptake value (SUV) index (i.e., the ratio of the tumor maximum SUV to the liver mean SUV) for IAC GGNs were also higher ( p = 0.001-0.022). Logistic regression analyses showed that the CT GGO and SUV index were independent predictors for IAC GGNs. The accuracy of CT GGO in combination with the SUV index for predicting IAC was 81.4% on a per-GGN basis and 85.4% on a per-patient basis. The combined HRCT and PET/CT modality had higher sensitivity and accuracy than did morphologic features, HRCT, and PET/CT measurement parameters alone ( p < 0.001). CONCLUSION. The combined HRCT and PET/CT modality is an effective method to preoperatively identify IAC in lung adenocarcinoma manifesting as GGNs 3 cm or smaller.

Cinematic rendering (CR) is a new 3D visualization methodology for volumetric diagnostic imaging including computed tomography (CT) datasets composed of isotropic voxels. CR produces photorealistic images with enhanced detail relative to other 3D visualization methods and realistic shadowing. In this review, we provide a number of examples of splenic pathology visualized with CR including conditions affecting the splenic vasculature, neoplasms, and accessory spleens. These examples are compared to 2D CT and traditional 3D CT techniques and the potential advantages of CR are highlighted. CR displays textural changes in the splenic parenchyma to particular advantage, and a portion of this review will be devoted to examples of how textural features can help distinguish intrapancreatic accessory spleens from neuroendocrine tumors., (Copyright © 2019 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

OBJECTIVE. The objective of our study was to investigate the potentials of enhanced dual-source dual-energy CT (DECT) and three-planar measurements for differentiating invasive pulmonary adenocarcinomas (IPAs) from preinvasive lesions appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS. Thirty-nine patients with 53 pGGNs who underwent enhanced dual-source DECT were included in this retrospective study. All pGGNs were pathologically confirmed and categorized into two groups: preinvasive lesions or IPAs. The traditional CT features of the pGGNs were evaluated on unenhanced images. Quantitative parameters were measured on iodine-enhanced images of dual-source DECT in three planes, and both intra- and interobserver reproducibility analyses were performed to assess the measurement reproducibility of quantitative parameters. To identify significant factors for differentiating IPAs from preinvasive lesions, we performed logistic regression analysis and ROC curve analysis. RESULTS. For traditional CT features, only lesion size and unenhanced CT attenuation value showed significant differences between preinvasive lesions and IPAs ( p < 0.05). Preinvasive lesions and IPAs exhibited significant differences in attenuation on virtual images, so-called "virtual HU" or "VHU," and the modified normalized iodine concentration (NIC) ( p < 0.05), and both intra- and interobserver agreement for the quantitative measurements were excellent. Multivariate logistic regression analysis revealed that larger lesion size (adjusted odds ratio [OR], 3.65) and higher modified NIC (adjusted OR, 19.01) were significant differentiators of IPAs from preinvasive lesions ( p < 0.05). ROC curve analysis revealed that modified NIC showed excellent performance (AUC, 0.924) and significantly higher performance than lesion size (AUC, 0.711) for differentiating IPAs from preinvasive lesions. CONCLUSION. In pGGNs, a lesion with a modified NIC value of more than 0.29 can be a very specific discriminator of IPAs from preinvasive lesions, and IPAs can be accurately and reliably differentiated from preinvasive lesions using enhanced dual-source DECT and three-planar measurements.

Objective: The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs)., Methods: This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set. Chest CT scans without contrast enhancement were performed in all patients preoperatively. The subjective CT features of the SSNs were reviewed and compared between the MIA and IAC groups. Each SSN was semisegmented with our in-house software, and entropy-related parameters were quantitatively extracted using another in-house software developed in the MATLAB platform. Logistic regression analysis and receiver operating characteristic analysis were performed to evaluate the diagnostic performances. Three diagnostic models including subjective model, entropy model, and combined model were built and analyzed using area under the curve (AUC) analysis., Results: There were 119 nonsolid nodules and 67 part-solid nodules. Significant differences were found in the subjective CT features among nodule type, lesion size, lobulated shape, and irregular margin between the MIA and IAC groups. Multivariate analysis revealed that part-solid type and lobulated shape were significant independent factors for IAC (P < 0.0001 and P < 0.0001, respectively). Three entropy parameters including Entropy-0.8, Entropy-2.0-32, and Entropy-2.0-64 were identified as independent risk factors for the differentiation of MIA and IAC lesions. The median entropy model value of the MIA group was 0.266 (range, 0.174-0.590), which was significantly lower than the IAC group with value 0.815 (range, 0.623-0.901) (P < 0.0001). Multivariate analysis revealed that the combined model had an excellent diagnostic performance with sensitivity of 88.2%, specificity of 73.0%, and accuracy of 82.1%. The AUC value of the combined model was significantly higher (AUC, 0.869) than that of the subjective model (AUC, 0.809) or the entropy model alone (AUC, 0.836) (P < 0.0001)., Conclusions: The CT-based entropy parameters could help assess the aggressiveness of pulmonary adenocarcinoma via quantitative analysis of intratumoral heterogeneity. The MIA can be differentiated from IAC accurately by using entropy-related parameters in peripheral pulmonary SSNs.

Purposes: Microscopic aggressive behaviors may be related with the prognosis of solid pseudopapillary neoplasms (SPNs). In this study, we investigate computed tomography (CT) features and differential diagnosis of aggressive and non-aggressive SPNs in pancreas., Materials and Methods: 122 patients with pathologically proven SPNs in pancreas were included. Patients' age, tumor site, texture, shape, margins, exophytic growth, capsule, calcification, hemorrhage, pancreatic duct dilatation or pancreatic parenchyma atrophy, peripancreatic infiltration or metastases, vascular encasement, and enhancement pattern were assessed. The diagnostic accuracy was analyzed by using the receiver operating characteristic curve (ROC)., Results: There were 30 aggressive SPNs and 92 non-aggressive SPNs. Aggressive SPNs showed significantly higher frequencies of an ill-defined margin, patient age > 40.5 years, and tumor size < 42.1 mm, but lower frequencies of complete capsule, hemorrhage compared with non-aggressive SPNs (p < 0.05). Lack of complete capsule and age > 40.5 years were independent risk factors of aggressive SPNs (odd ratio 7.08 and 3.1, respectively). When we applied the two predictors in the logistic regression model, the area under the curve (AUC) was 0.77 with sensitivity of 86.7% and specificity of 55.4%., Conclusion: Size less than 42.1 mm, lack of complete capsule, ill-defined, and absent bleeding are useful CT imaging features for predicating aggressive SPNs. Patient age > 40.5 years and lack of complete capsule showed acceptable diagnostic performance for discriminating aggressive from non-aggressive SPNs.

Objective: With the hypothesis that the combination of CT, conventional, and functional MRI can indicate a possible diagnosis of skull lymphoma, this study aimed to systematically explore CT, conventional, and functional MRI features of this rare entity., Materials and Methods: This retrospective study included eight patients with pathologically confirmed skull lymphomas. CT and conventional MRI findings, including the location, size, attenuation/signal intensity, cystic/necrosis, hemorrhage, calcification, enhancement, skull change, brain parenchyma edema and adjacent structure invasion, were reviewed. We also reviewed multi-parametric functional MR imaging features obtained from diffusion-weighted imaging (DWI, n = 4), susceptibility-weighted imaging (SWI, n = 3) and dynamic susceptibility-weighted contrast-enhanced perfusion-weighted imaging (DSC-PWI, n = 1)., Results: The eight patients in this series consisted of five males and three females, with a mean age of 51.1 years. All skull lymphomas showed the tumors extending to extra- and intra-cranial spaces with permeative destruction of the intervening skull. Intratumoral cystic/necrosis was seen in one case. Hemorrhage or calcification was absent. Dural mater infiltration was detected in all cases. Two clivus lymphomas encased internal carotid artery without narrowing the lumen. Three cases invaded brain parenchyma with moderate edema. The tumors demonstrated high signal on DWI with low ADC values comparing to muscles. SWI images showed little intratumoral hemorrhage and vessel. Low relative cerebral blood volume (rCBV) value was detected., Conclusions: Skull lymphomas commonly presented as a homogenous solid tumor extending either intra- or extra-cranially with permeative bone destruction. Restricted diffusion, little intratumoral susceptibility signal, and lower perfusion may indicate a specific diagnosis. Multi-parametric functional MRI may be a promising tool for the diagnosis of skull lymphomas.

Purpose: To establish a predictive model for surgical resection of invasive pulmonary adenocarcinoma (IPA) presenting as ground-glass nodules (GGNs) based on a radiomics nomogram., Methods: The CT images of 239 patients with GGNs were collected, of which 160 cases were included in the training set to construct the predictive model and 79 cases were included in the validation set to verify the established predictive model. The least absolute shrinkage and selection operator algorithm was used to select the radiomic features and construct the radiomics tagging. The predictive model for the surgical resection of IPA was constructed using the radiomics nomogram., Results: The presence of IPA showed significant correlations with seven radiomics features (P < .01), which were the independent predictors. The predictive model constructed using the radiomics features performed well on the training set (area under the curve [AUC] 0.792, 95% confidence interval [CI]: 0.720-0.864) and the validation set (AUC 0.773, 95% CI: 0.668-0.877). The predictive model constructed using the clinical information alone was relatively less effective (AUC 0.711, 95% CI: 0.634-0.787). The predictive model constructed by integrating the radiomics features into the clinical information using the radiomics nomogram showed the best predictive ability and calibration in the training set (AUC 0.831, 95% CI: 0.765-0.897) and the validation set (AUC 0.816, 95% CI: 0.724-0.909). Decision curve analysis showed that radiomics nomogram has a certain clinical value., Conclusion: The predictive model for surgical resection of IPA constructed by integrating the radiomics features and the clinical information based on the radiomics nomogram can help clinicians control the operative node and reduce the occurrence of overtreatment., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination., Materials and Methods: A database of 515 harmonized thyroid CT examinations was used, of which information regarding cartilage abnormality was provided for 326. The process consisted of determining image abnormality and, from these preprocessed images, finding the best learning algorithm to appropriately characterize thyroid cartilage as normal or abnormal. CT images were cropped to be centered around the cartilage in order to focus on the relevant area. New images were generated from the originals by applying simple transformations in order to augment the database. Characterizations of cartilage abnormalities were made using transfer learning, by using the architecture of a pre-trained neural network called VGG16 and adapting the final layers to a binary classification problem., Results: The best algorithm yielded an area under the receiving operator characteristic curve (AUC) of 0.72 on a sample of 82 thyroid test images. The sensitivity and specificity of the abnormality detection were 83% and 64% at the best threshold, respectively. Applying the model on another independent sample of 189 new thyroid images resulted in an AUC of 0.70., Conclusion: This study demonstrates the feasibility of using a deep learning-based abnormality detection system to evaluate thyroid cartilage from CT examinations. However, although promising results, the model is not yet able to match an expert's diagnosis., (Copyright © 2019. Published by Elsevier Masson SAS.)

Purpose: To perform contrast analysis of the relationship between high-resolution computed tomography (HRCT) signs and new pathologic classification of small GGNs-like lung adenocarcinoma., Materials and Methods: The HRCT data from 145 pathologically confirmed cases of small GGNs of lung adenocarcinoma were analysed retrospectively. The 145 cases of GGNs were divided into pre-invasive (PI) group (n = 46), micro-invasive adenocarcinoma (MIA) group (n = 48), and invasive adenocarcinoma (IAC) group (n = 51). HRCT imaging sign of GGNs in each group was assessed and compared., Results: Significant differences in GGN size were found among the three groups (P < 0.05). The presence of a tumour-lung interface in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05), but no significant difference was found between the MIA and IAC groups. The presence of a pleural indentation sign in the IAC group was significantly higher than that in the other two groups (P < 0.05), but no significant difference was noted between the latter two groups. Significant differences were found in the lobulated and spicule signs among the three groups (P < 0.05). The presence of a microvascular sign in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05). No significant difference was found in the GGN density, vacuole sign, air bronchus sign and notch sign among the three groups., Conclusions: The HRCT signs of GGNs could be used to differentiate among pre-invasive lesions, micro-invasive lesions and invasive lung adenocarcinoma.

Objective: To determine the diagnostic accuracy of contrast enhanced computed tomography (CECT) of head and neck in detecting adjacent bone invasion due to squamous cell carcinoma of buccal mucosa using histopathology as gold standard., Study Design: Cross sectional / descriptive study., Place and Duration of Study: Department of Radiology, Dow Medical College, Civil Hospital, Karachi, from September 2015 to October 2016., Methodology: A total of 116 patients clinically suspected of oral cavity squamous cell carcinoma (OCSCC) involving buccal mucosa was included. After taking proper history, all these patients referred to Radiology Department for contrast enhanced contiguous thin slice sections of 3-5 mm the primary site of the lesion prior to surgery. Reconstruction of all images done by using bone algorithm. The diagnostic accuracy of CECT was analysed against the gold standard of histopathology. Statistical analyses were carried out by using SPSS 21. Mean ±SD were calculated for quantitative variable like age. For qualitative variables, frequencies and percentages were calculated. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were computed for CECT, taking histopathology as gold standard., Results: Eighty-three male (71%) and 33 female (28%) patients. Mean age was 46.66±10.77 years. Mean duration of sign and symptoms was 3.47 ±1.54 months. Sensitivity, specificity, PPV, NPV and accuracy were 69.2%, 78.4%, and 80.4%, 66.7% and 73.3%, respectively., Conclusion: CECT is a reliable technique for detecting adjacent bone invasion due to squamous cell carcinoma of buccal mucosa by using 3-5 mm thin-section CT reconstructed with bone algorithm.

Objectives/hypothesis: There is little evidence to support the removal of thyroid tissue during total laryngectomy. Although oncological control of the tumor is the priority, thyroidectomy can lead to hypothyroidism and hypoparathyroidism. This study aimed to test the usefulness of preoperative computed tomography in predicting histological invasion of the thyroid., Study Design: Ambispective cohort study., Methods: All patients undergoing total laryngectomy for squamous cell carcinoma at one center from 2006 to 2016 were included. Data were recorded prospectively as part of the patients' standard care, but were collated retrospectively, giving this study an ambispective design. The histology report for thyroid invasion was taken as the gold standard. The computed tomography report was categorized by invasion of tumor into intralaryngeal, laryngeal cartilage involvement, and extralaryngeal tissues., Results: Seventy-nine patients were included. Nine patients had thyroid involvement on histology, translating to an incidence of 11.29% in this population. The positive predictive value for cartilage involvement on computed tomography for thyroid invasion was 52.9% (95% confidence interval [CI]: 28.5%-76.1%) and the negative predictive value was 100% (95% CI: 92.7%-100%).The positive predictive value for extralaryngeal spread on computed tomography for thyroid involvement was 100% (95% CI: 62.9%-100%), and the negative predictive value was also 100% (95% CI: 93.5%-100%)., Conclusions: This study has shown that preoperative computed tomography is an effective method of ruling out thyroid gland invasion. The absence of extralaryngeal spread on computed tomography has been shown to be the most useful finding, with a high negative predictive value and a narrow 95% CI., Level of Evidence: 4. Laryngoscope, 128:1099-1102, 2018., (© 2017 The American Laryngological, Rhinological and Otological Society, Inc.)

Purpose: The purpose of this study was to evaluate the usefulness of computed tomography-texture analysis (CTTA) in differentiating between in-situ and minimally-invasive from invasive adenocarcinomas in subsolid lung nodules (SSLNs)., Material and Methods: Two radiologists retrospectively reviewed 49 SSLNs in 44 patients. There were 27 men and 17 women with a mean age of 63±7 (SD) years (range: 47-78years). For each SSLN, type (pure ground-glass or part-solid) was assessed by consensus and CTTA was conducted independently by each observer using a filtration-histogram technique. Different filters were used before histogram quantification: no filtration, fine, medium and coarse, followed by histogram quantification using mean intensity, standard deviation (SD), entropy, mean positive pixels (MPP), skewness and kurtosis., Results: We analyzed 13 pure ground-glass and 36 part-solid nodules corresponding to 16 adenocarcinomas in-situ (AIS), 5 minimally invasive adenocarcinomas (MIA) and 28 invasive adenocarcinomas (IVA). At uni- and multivariate analysis CTTA allowed discriminating between IVAs and AIS/MIA (P<0.05 and P=0.025, respectively) with the following histogram parameters: skewness using fine textures and kurtosis using coarse filtration for pure ground-glass nodules, and SD without filtration for part-solid nodules., Conclusion: CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort., (Copyright © 2018 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: Suspicion of mandibular invasion directly influences perioperative strategy, requiring marginal or segmental mandibulectomy, or reconstruction in some cases. This has a considerable impact on outcome and quality of life. The aim of this study was to evaluate the accuracy of magnetic resonance and computed tomography in the prediction of mandibular invasion in patients with oral cavity cancer., Method: A systematic review was conducted, including diagnostic studies comparing magnetic resonance imaging with computed tomography in the prediction of bone invasion. Sensitivity, specificity, positive and negative likelihood values and summary receiver operating characteristic (sROC) curves were calculated., Results: The electronic and manual search identified 346 articles. Of these, 11 studies were included in the systematic review for a total of 477 patients. The sensitivity, specificity, and positive and negative likelihood values for MRI were 78%, 86%, 5.29 and 0.23, respectively. For CT, they were 76%, 89%, 6.00 and 0.28, respectively. The sROC curves for MRI and CT were 82.3% and 82.5%, respectively., Conclusion: No superiority was observed between the diagnostic methods regarding mandibular invasion detection., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Pancreatic ductal adenocarcinoma continues to be a highly lethal disease, despite advances in modern medicine. Curative surgical options continue to carry significant morbidity and offer little improvement in overall 5-year survival. Currently, imaging plays an essential role in the pre-operative evaluation of patients who are undergoing evaluation for resection. However, some pancreatic cancers have particularly aggressive biology, despite appearing resectable by conventional imaging criteria. Imaging biomarkers that serve as surrogates for tumors with such aggressive phenotype have been recently described, namely duodenal invasion and extrapancreatic perineural invasion. In this pictorial review, we will summarize key concepts of extrapancreatic perineural invasion, describe its association with a poor prognosis, and highlight the role of imaging in its detection.

Despite advances in oncologic and imaging technology, pancreatic ductal adenocarcinoma remains a highly deadly disease. The only curative option, pancreaticoduodenectomy or pancreatectomy, carries a significant morbidity. Current imaging plays a role in pre-operative staging to determine the probability of achieve disease-free margins. However, a small but not insignificant number of pancreatic cancers have a relatively higher aggressive biology, despite being resectable based on traditional criteria. Recently, imaging biomarkers that serve as a surrogate for tumors with such aggressive phenotype have been described. These include duodenal invasion and extrapancreatic perineural invasion. This review will focus on the former highlighting the summary of literature supporting duodenal invasion as a surrogate for aggressive disease as well as review its MDCT imaging features.

Background: Radiographic concern for lymphatic extranodal extension (ENE) impacts upfront management decisions for patients with human papilloma virus (HPV) oropharyngeal squamous cell carcinoma (OPSCC). Therefore, we set out to evaluate the accuracy of preoperative contrast-enhanced computed tomography (CECT) to predict major ENE (> 2 mm)., Methods: Twenty-seven consecutive patients with HPV-associated OPSCC who presented at our institutional multidisciplinary tumor board were staged radiographically with positron emission tomography (PET/CT) and CECT, and underwent primary transoral robotic resection and neck dissection. CECT imaging results were correlated with pathologic ENE (pENE)., Results: CECT specificity for all pENE was 69 and 75% for radiologist 1 and 2, respectively. For pENE > 2 mm, the sensitivities were 88 and 100%, but specificities were 52.6 and 63.2%. Positive predictive values (PPV) were 43.8 and 53.3%; negative predictive values were 90.9 and 100%. On logistic regression analysis, only size ≥3 cm (OR 4.7-5.4, p < 0.02, 95% CI 1.3-44.0) demonstrated significant correlation with major ENE > 2 mm., Conclusions: Preoperative imaging for HPV-associated OPSCC had a PPV for pENE > 2 mm of 44-55%, based on any interruption in the capsule or invasion into the perinodal fat. The PPV is low and equipoise in treatment decision making for patients with HPV-associated OPSCC may require other imaging characteristics., (© 2018 S. Karger AG, Basel.)

Objective: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type., Materials and Methods: The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups. The heterogeneity metrics were used to train a SVM to learn and predict the lesion type., Results: Fifty of 57 and 51 of 57 heterogeneity metrics showed statistically significant differences among the four GGN groups on unenhanced and contrast-enhanced CT scans, respectively. The SVM predicted lesion type with greater accuracy than did three expert radiologists. The accuracy of classifying the GGNs into the four groups on the basis of the SVM algorithm was 70.9%, whereas the accuracy of the radiologists was 39.6%. The accuracy of SVM in classifying the AIS and MIA nodules was 73.1%, and the accuracy of the radiologists was 35.7%. For indolent versus invasive lesions, the accuracy of the SVM was 88.1%, and the accuracy of the radiologists was 60.8%. We found that contrast enhancement does not significantly improve the differential diagnosis of GGNs., Conclusion: Compared with the GGN classification done by the three radiologists, the SVM trained regarding all the heterogeneity metrics showed significantly higher accuracy in classifying the lesions into the four groups, differentiating between AIS and MIA and between indolent and invasive lesions. Contrast enhancement did not improve the differential diagnosis of GGNs.

Objective: To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative CT, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule., Methods: Six subjects (age: 75 ± 11 years) with pre-operative CT and surgically excised ground-glass nodules (size: 22.5 ± 5.1 mm) with a significant invasive adenocarcinomatous component (>5 mm) were included. The pathologist outlined disease extent on digitized histology specimens; two radiologists and a pulmonary critical care physician delineated the entire nodule on CT (in-plane resolution: <0.8 mm, inter-slice distance: 1-5 mm). We introduced a novel reconstruction approach to localize histology slices in 3D relative to each other while using CT scan as spatial constraint. This enabled the spatial mapping of the extent of tumour invasion from histology onto CT., Results: Good overlap of the 3D reconstructed histology and the nodule outlined on CT was observed (65.9 ± 5.2%). Reduction in 3D misalignment of corresponding anatomical landmarks on histology and CT was observed (1.97 ± 0.42 mm). Moreover, the CT attenuation (HU) distributions were different when comparing invasive and in situ regions., Conclusion: This proof-of-concept study suggests that our fusion method can enable the spatial mapping of the invasive adenocarcinomatous component from 2D histology slices onto in vivo CT., Key Points: • 3D reconstructions are generated from 2D histology specimens of ground glass nodules. • The reconstruction methodology used pre-operative in vivo CT as 3D spatial constraint. • The methodology maps adenocarcinoma extent from digitized histology onto in vivo CT. • The methodology potentially facilitates the discovery of CT signature of invasive adenocarcinoma.

Objective: To evaluate CT and histopathologic features of lung adenocarcinoma with pure ground-glass nodule (pGGN) ≤10 mm in diameter., Methods: CT appearances of 148 patients (150 lesions) who underwent curative resection of lung adenocarcinoma with pGGN ≤10 mm (25 atypical adenomatous hyperplasias, 42 adenocarcinoma in situs, 38 minimally invasive adenocarcinomas, and 45 invasive pulmonary adenocarcinomas) were analyzed for lesion size, density, bubble-like sign, air bronchogram, vessel changes, margin, and tumour-lung interface. CT characteristics were compared among different histopathologic subtypes. Univariate and multivariate analysis were used to assess the relationship between CT characteristics of pGGN and lesion invasiveness, respectively., Results: There were statistically significant differences among histopathologic subtypes in lesion size, vessel changes, and tumour-lung interface (P<0.05). Univariate analysis revealed significant differences of vessel changes, margin and tumour-lung interface between preinvasive and invasive lesions (P<0.05). Logistic regression analysis showed that the vessel changes, unsmooth margin and clear tumour-lung interface were significant predictive factors for lesion invasiveness, with odds ratios (95% CI) of 2.57 (1.17-5.62), 1.83 (1.25-2.68) and 4.25 (1.78-10.14), respectively., Conclusion: Invasive lesions are found in 55.3% of subcentimeter pGGNs in our cohort. Vessel changes, unsmooth margin, and clear lung-tumour interface may indicate the invasiveness of lung adenocarcinoma with subcentimeter pGGN., Key Points: • Invasive lesions were found in 55.3% of lung adenocarcinomas with subcentimeter pGGNs • Lesion size, vessel changes, and tumour-lung interface showed different among histopathologic subtypes • Vessel changes, unsmooth margin and clear tumour-lung interface were predictors for lesion invasiveness.

The aim of the study is to evaluate the use of the tumor border in peripheral non-small cell lung cancer (NSCLC) as an indicator of pleural invasion.This retrospective study was performed at a single tertiary center. The analysis of 136 patients with peripheral NSCLC included 101 (74.3%) patients with pathologically proven pleural invasion and 35 (25.7%) patients without pleural invasion. The tumor borders on conventional computed tomography (CT) were classified into 5 types on lung window setting: type 1, S or reverse S border with a blunt angle; type 2, sharp angle; type 3, concave border with a blunt angle; type 4, straight border with a perpendicular angle; and type 5, convex border with a perpendicular or blunt angle. In patients with more than 1 tumor border type, the priority was type 5, 4, 3, 2, and 1. Blunt angle, pleural contact >3 cm, and adjacent pleural thickening were also recorded for comparison with pleural invasion of peripheral tumors.Tumor border types 2 and 5 significantly differed between patients with and without pleural invasion (P = .001 and P < .001, respectively). Patients with and without pleural invasion did not significantly differ in tumor border type 1, tumor border type 3, tumor border type 4, blunt angle, pleural contact >3 cm, or pleural thickening. Tumor border type 5 was a moderate indicator of pleural invasion with positive LR, 5.20; accuracy, 57%; sensitivity, 45%; specificity, 91%; PPV, 94%; and NPV, 36%. Tumor border type 2 was a weak indicator of pleural invasion with positive LR, 0.51; accuracy, 34%; sensitivity, 34%; specificity, 34%; PPV, 60%; and NPV, 15%.Tumor border type 5 has a high PPV and high specificity for predicting pleural invasion by peripheral NSCLC.

Purpose: The purpose of the article is to compare the diagnostic performance about radiation dose and image quality of low-dose CT with iterative reconstruction algorithm (iDose4) and standard-dose CT in the assessment of mesorectal fascia (MRF) invasion in rectal cancer patients., Materials and Methods: Ninety-one patients with biopsy-proven primary rectal adenocarcinoma underwent CT staging: 42 underwent low-dose CT, 49 underwent standard CT protocol. Low-dose contrast-enhanced MDCT scans were performed on a 256 (ICT, Philips) scanner using 120 kV, automated mAs modulation, iDose4 iterative reconstruction algorithm. Standard-dose MDCT scans were performed on the same scanner with 120 kV, 200-300 mAs. All patients underwent a standard lower abdomen MR study (on 1.5T magnet), including multiplanar sequences, considered as reference standard. Diagnostic accuracy of MRF assessment was determined on CT images for both CT protocols and compared with MRI images. Dose-length product (DLP) and CT dose index (CTDI) calculated for both groups were compared and statistically analyzed., Results: Low-dose protocol with iDose4 showed high diagnostic quality in assessment of MRF with significant reduction (23%; p = 0.0081) of radiation dose (DLP 2453.47) compared to standard-dose examination (DLP 3194.32)., Conclusions: Low-dose protocol combined with iDose4 reconstruction algorithm offers high-quality images, obtaining significant radiation dose reduction, useful in the evaluation of MRF involvement in rectal cancer patients.

Objective: The objective of our study was to determine whether visceral pleural invasion (VPI) of T1-sized peripheral pulmonary adenocarcinomas manifesting as subsolid nodules (SSNs) abutting the pleural surface or associated with pleural tags can be predicted., Materials and Methods: Our study population consisted of 188 T1-sized peripheral pulmonary adenocarcinomas that appeared as SSNs (24 pure ground-glass nodules [GGNs] and 164 part-solid nodules) and underwent surgical resection between January 2007 and December 2013. Logistic regression analysis was performed to identify significant factors in predicting VPI., Results: VPI occurred in 36 of 188 adenocarcinomas (19.1%). There were no cases of VPI in patients with pure GGNs. In part-solid nodules, there were significant differences regarding the presence of pleural contact, presence of pleural thickening, presence of solid portion abutting the pleura, nodule size, solid portion size, solid proportion, interface length, and length of the solid portion contacting the pleura (p < 0.05). Multivariate analysis revealed pleural contact (p < 0.001), pleural thickening (p = 0.003), solid proportion greater than 50% (p = 0.002), and nodule size greater than 20 mm (p = 0.015) as significant independent predictive features for VPI with adjusted odds ratios of 8.300, 3.966, 4.636, and 2.993, respectively., Conclusion: In part-solid nodules, the CT features of pleural contact, pleural thickening, solid proportion greater than 50%, and nodule size greater than 20 mm were shown to be significant indicators of VPI by T1-sized peripheral adenocarcinomas.

Objective: To evaluate the adequacy of ultrasonography (US) and computed tomography (CT) in the assessment of thyroid cartilage invasion in patients with airway cancer., Materials and Methods: Sixty-two consecutive patients referred to our institute underwent US and CT to stage laryngeal ( n  = 27) or hypopharyngeal ( n  = 35) cancer in this prospective study. Two radiologists, who were blinded to the patients' clinical histories and histopathology, evaluated thyroid cartilage invasion on US and CT separately and independently. Fifty-eight of the 64 patients (90%) underwent surgery. The histopathologic findings were used as the standard of reference for comparison and statistical analysis., Results: For thyroid cartilage invasion, the detection rate on CT and US was 98%. CT achieved a sensitivity of 91% and a specificity of 75%, while US attained a sensitivity of 98% and a specificity of 75%. The difference between CT and US in terms of sensitivity was not statistically significant., Conclusion: US and CT have high diagnostic performance in evaluating thyroid cartilage invasion. US is more sensitive than CT in diagnosing invasion of the thyroid cartilage; however, the difference is not statistically significant. US can be used to solve the diagnostic dilemma of the presence or absence of cartilage invasion when CT is inconclusive, as CT is more widely used in staging laryngeal and hypopharyngeal cancers.

Purpose: To evaluate the role of medical imaging in predicting local recurrence of giant cell tumour of bone (GCTB) by assessing the preoperative imaging features of GCTB around the knee., Methods: Forty-eight consecutive GCTBs in the proximal tibia and distal femur treated with curettage were prospectively enrolled. Patients were grouped in terms of their imaging features on radiography, computed tomography (CT) and magnetic resonance imaging (MRI). All patients were followed up for at least two years after surgery. The association between preoperative imaging features and local recurrence was investigated. Imaging features were retrospectively studied by correlation analysis. The differences between rates were tested by the Chi square and Fisher exact tests; independent factors were determined by multivariate logistic regression analysis., Results: Cystic change and adjacent soft tissue invasion were associated with a higher rate of local recurrence compared to the negative groups (P < 0.05). Cystic change was identified as an independent risk factor for local recurrence of GCTB (P < 0.05). Expansibility was correlated with the "soap bubble" sign and the fluid-fluid level (P < 0.05); the "soap bubble" sign was correlated with osteosclerosis and the fluid-fluid level (P < 0.05); cortical bone involvement was correlated with adjacent soft tissue invasion (P < 0.05); and cystic change was correlated with the fluid-fluid level (P < 0.05)., Conclusion: Cystic change was an independent risk factor for local recurrence of GCTB. Adjacent soft tissue invasion might indirectly relate to local relapse. A cluster of association relationships between imaging features was revealed.

Objective: This study aimed to investigate whether there is a correlation between the computed tomography-detected extramural venous invasion (ctEMVI) and disease-free survival (DFS) in patients with gastric cancer using pathologic lymphovascular invasion as a reference standard., Methods: We retrospectively reviewed 153 patients with gastric cancer who underwent computed tomography during 1 year. Differences in pathological findings between the ctEMVI-positive and ctEMVI-negative groups were analyzed. Disease-free survival was estimated using the Kaplan-Meier method. Factors affecting DFS were analyzed with the Cox proportional hazard model., Results: The ctEMVI-positive group was correlated more with lymphovascular invasion (P = 0.008). The 1- and 2-year DFS rates were 92% and 80%, respectively, in the ctEMVI-negative group, but 77% and 54%, respectively, in the ctEMVI-positive group. A multivariate analysis revealed that tumor size, ctEMVI, and pathological stage remained associated with DFS (Ps = 0.037, 0.015, and 0.002, respectively)., Conclusions: The ctEMVI was an independent prognostic factor for worse DFS in patients with gastric cancer.

Aim: The aim of this study is to evaluate the information given by contrast-enhanced computer tomography (CECT) and ultrasound endoscopy (EUS) regarding vascular involvement of cephalo-pancreatic cancer, data compared with intraoperative findings., Material and Methods: We choose to analyze the most often interested vessels by tumor development, such as superior mesenteric artery (SMA), superior mesenteric vein (SMV) and portal vein (PV). The patients included in the study (n=425) had a cephalo-pancreatic tumor diagnosed in our Institute and a positive histology for pancreatic adenocarcinoma. The exclusion criteria were: tumors in sites other than the head of the pancreas (including metastases); tumor involvement of common hepatic artery, celiac trunk, inferior cava vein or aorta; CECT or EUS diagnosis performed in another center; and a delay of more than 35 days between the diagnostic imaging and surgery., Results: In diagnosing SMA invasion CECT had an accuracy of 84,92% and EUS had an accuracy of 87,39%. In diagnosing PV and SMV involvement, CECT had an accuracy of 84,83% and EUS had an accuracy of 92,17%. The accuracy of the two combined examinations in diagnosing vascular invasion was 93%., Conclusons: Both types of examination have showed good accuracies in diagnosing vascular invasion separately. A combination of the two may be used when the CECT result is uncertain as it provides a higher chance of a correct diagnosis., Key Words: Pancreatic cancer, Resectability criteria, Vascular invasion.