Impact of radiomics features, pulmonary emphysema score and muscle mass on the rate of pneumothorax and chest tube insertion in CT-guided lung biopsies.

Iatrogenic pneumothorax is a relevant complication of computed tomography (CT)-guided percutaneous lung biopsy. The aim of the present study was to analyze the prognostic significance of texture analysis, emphysema score and muscle mass derived from CT-imaging to predict postinterventional pneumothorax after CT-guided lung biopsy. Consecutive patients undergoing CT-guided percutaneous lung biopsy between 2012 and 2021 were analyzed. Multivariate logistic regression analysis included clinical risk factors and CT-imaging features to detect associations with pneumothorax development. Overall, 479 patients (178 females, mean age 65 ± 11.7 years) underwent CT-guided percutaneous lung biopsy of which 180 patients (37.5%) developed pneumothorax including 55 patients (11.5%) requiring chest tube placement. Risk factors associated with pneumothorax were chronic-obstructive pulmonary disease (COPD) (p = 0.03), age (p = 0.02), total lung capacity (p < 0.01) and residual volume (p = 0.01) as well as interventional parameters needle length inside the lung (p < 0.001), target lesion attached to pleura (p = 0.04), and intervention duration (p < 0.001). The combined model demonstrated a prediction accuracy of the occurrence of pneumothorax with an AUC of 0.78 [95%CI: 0.70-0.86] with a resulting sensitivity 0.80 and a specificity of 0.66. In conclusion, radiomics features of the target lesion and the lung lobe CT-emphysema score are predictive for the occurrence of pneumothorax and need for chest insertion after CT-guided lung biopsy.

CT Texture analysis and CT scores for characterization of fluid collections.

BACKGROUND: Texture analysis derived from Computed tomography (CT) might be able to better characterize fluid collections undergoing CT-guided percutaneous drainage treatment. The present study tested, whether texture analysis can reflect microbiology results in fluid collections suspicious for septic focus. METHODS: Overall, 320 patients with 402 fluid collections were included into this retrospective study. All fluid collections underwent CT-guided drainage treatment and were microbiologically evaluated. Clinically, serologically parameters and conventional imaging findings as well as textures features were included into the analysis. A new CT score was calculated based upon imaging features alone. Established CT scores were used as a reference standard. RESULTS: The present score achieved a sensitivity of 0.78, a specificity of 0.69, area under curve (AUC 0.82). The present score and the score by Gnannt et al. (AUC 0.81) were both statistically better than the score by Radosa et al. (AUC 0.75). Several texture features were statistically significant between infected fluid collections and sterile fluid collections, but these features were not significantly better compared with conventional imaging findings. CONCLUSIONS: Texture analysis is not superior to conventional imaging findings for characterizing fluid collections. A novel score was calculated based upon imaging parameters alone with similar diagnostic accuracy compared to established scores using imaging and clinical features.

CT Texture Analysis of Perihilar Cholangiocarcinoma-Associations With Tumor Grading, Tumor Markers and Clinical Outcome.

BACKGROUND: Texture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis. AIMS: The present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors. METHODS: This retrospective study included 22 patients (10 females, 45%) with a mean age of 71.8 ± 8.7 years. Texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. Survival and clinical parameters were used as primary study outcomes. RESULTS: In discrimination analysis, "S(1,1)SumVarnc" was statistically significantly different between patients with long-term survival and nonlong-term survival (mean 275.8 ± 32.6 vs. 239.7 ± 26.0, p = 0.01). The first-order parameter "skewness" was associated with the tumor marker "carcinoembryonic antigen" (CEA) (r = -0.7, p = 0.01). A statistically significant correlation of the texture parameter "S(5,0)SumVarnc" with tumor grading was identified (r = -0.6, p < 0.01). Several other texture features correlated with tumor markers CA-19-9 and AFP, as well as with T and N stage of tumors. CONCLUSION: Several texture features derived from CT images were associated with tumor characteristics and survival in patients with perihilar cholangiocarcinomas. CT texture features could be used as valuable novel imaging markers in clinical routine.

Diagnostic accuracy and reliability of CT-based Node-RADS for colon cancer.

OBJECTIVE: The Node-RADS classification was recently published as a classification system to better characterize lymph nodes in oncological imaging. The present analysis investigated the diagnostic benefit of the Node-RADS classification of staging computed tomography (CT) images to categorize and stage lymph nodes in patients with colon cancer. MATERIALS AND METHODS: All patients were surgically resected and the lymph nodes were histopathological analyzed. All investigated lymph nodes were scored in accordance to the Node-RADS classification by two experienced radiologists. Interreader variability was assessed with Cohen's kappa analysis, discrimination analysis was performed with Mann-Whitney-U test and diagnostic accuracy was assessed with receiver-operating characteristics (ROC) curve analysis. RESULTS: Overall, 108 patients (n = 49 females, 45.3%) with a mean age of 70.08 ± 14.34 years were included. In discrimination analysis, the total Node-RADS score showed statistically significant differences between N- and N + stage (for reader 1: mean 1.89 ± 1.09 score for N- versus 2.93 ± 1.62 score for N+, for reader 2: 1.33 ± 0.48 score for N- versus 3.65 ± 0.94 score for N+, p = 0.001, respectively). ROC curve analysis for lymph node discrimination showed an area under the curve of 0.68. A threshold value of 2 resulted in a sensitivity of 0.62 and a specificity of 0.71. CONCLUSION: Node-RADS score derived from staging CT shows only limited diagnostic accuracy to correctly predict nodal positivity in colon cancer. The interreader variability seems to be high and should question the clinical translation for this tumour entity.

Visceral obesity determined in routine preoperative CT scans predicts risk of postoperative burst abdomen.

Burst abdomen (BA) remains a severe postoperative complication after abdominal surgery. Obesity is a known risk factor for postoperative complications but objective parameters such as body mass index fail to predict BA after abdominal surgery. In recent literature, CT-derived body composition assessment could predict obesity-related diseases and surgical site infections. We report data from the institutional wound register, comparing patients with BA to a subgroup of patients without BA. The CT images were evaluated for intraabdominal and subcutaneous fat tissues. Univariate and multivariate risk factor analysis was performed in order to evaluate CT-derived obesity parameters as risk factor for BA. 92 patients with BA were compared to 32 controls. Patients with BA had significantly more visceral obesity (VO; p < 0.001) but less subcutaneous obesity (SCO) on CT scans. VO and SCO both were positively correlated with BMI (r = 0.452 and 0.572) but VO and SCO were inversely correlated (r = -0.189). Multivariate analysis revealed VO as significant risk factor for postoperative BA (OR 1.257; 95% CI 1.084-1.459; p = 0.003). Our analysis of patients with postoperative BA revealed VO as major risk factor for postoperative BA. Thus, preoperative CT scans gives valuable information on possible risk stratification.

Computed tomography texture analysis to discriminate fungal and non-fungal infected fluid collections.

Purpose: Texture analysis of computed tomography (CT) can aid in characterization of fluid collections providing biomarkers. The present study tested whether texture analysis can discriminate between fungal or non-fungal infection in patients undergoing CT-guided percutaneous drainage treatment. Approach: Overall, 214 patients [(n=76 females, 35.5%); mean age 62±14 years and range 20 to 94 years] with 255 fluid collections were included in the analysis. All patients underwent CT-guided drainage treatment and were evaluated with microbiological analysis. CT texture analysis was performed with the MaZda package. Results: Only three of the investigated CT texture features were statistically significant different between the groups, namely kurtosis (p=0.04), S(3,3)InvDfMom (p=0.02), and S(5,-5)DifEntrp (p=0.003). These texture features were further investigated by the receiver operating characteristic curve. S(3,3)InvDfMom achieved the highest accuracy with an area under the curve of 0.62, resulting in a sensitivity of 0.66 and a specificity of 0.57. Conclusion: Some CT texture features were different between fungal and non-fungal infected fluid collections. The diagnostic overlap is large, which could reduce the clinical benefit. Further studies are needed to identify the possible diagnostic benefit of texture analysis in these patients.

CT Texture Analysis and Node-RADS CT Score of Lymph Nodes in Patients With Perihilar Cholangiocarcinoma.

BACKGROUND/AIM: Texture analysis can provide quantitative imaging markers from computed tomography (CT) images. The Node-RADS classification was recently published as a classification system to better characterize lymph nodes in oncological imaging. The present analysis investigated the diagnostic benefit of CT texture analysis and the Node-RADS classification to categorize and stage lymph nodes in patients with perihilar cholangiocarcinoma. PATIENTS AND METHODS: Overall, 25 patients (n=9 females, 36%) with a mean age of 72.4±8.1 years were included. All patients were surgically resected and the lymph nodes were histopathologically analyzed. CT-texture analysis was performed with the Mazda package. All investigated lymph nodes were scored in accordance with the Node-RADS classification. RESULTS: Regarding lymph node discrimination (N- versus N+), Node-RADS classification achieved an area under the curve (AUC) of 0.86 resulting in a sensitivity of 78% and a specificity of 86%. Multiple investigated texture features were different between negative and positive lymph nodes. The "S(0,1)SumVarnc" achieved the best AUC of 0.75 resulting in a sensitivity of 0.91 and a specificity of 0.67. Correlation analysis showed various statistically significant associations between CT texture features and Node-RADS score. CONCLUSION: Several CT texture features and the Node-RADS score derived from preoperative staging CT were associated with the malignancy of the hilar lymph nodes and might aid for preoperative staging. This could change surgical treatment planning in hilar cholangiocarcinoma.

CT texture analysis and node-RADS CT score of mediastinal lymph nodes - diagnostic performance in lung cancer patients.

BACKGROUND: Texture analysis derived from computed tomography (CT) can provide clinically relevant imaging biomarkers. Node-RADS is a recently proposed classification to categorize lymph nodes in radiological images. The present study sought to investigate the diagnostic abilities of CT texture analysis and Node-RADS to discriminate benign from malignant mediastinal lymph nodes in patients with lung cancer. METHODS: Ninety-one patients (n = 32 females, 35%) with a mean age of 64.8 ± 10.8 years were included in this retrospective study. Texture analysis was performed using the free available Mazda software. All lymph nodes were scored accordingly to the Node-RADS classification. All primary tumors and all investigated mediastinal lymph nodes were histopathologically confirmed during clinical workup. RESULTS: In discrimination analysis, Node-RADS score showed statistically significant differences between N0 and N1-3 (p < 0.001). Multiple texture features were different between benign and malignant lymph nodes: S(1,0)AngScMom, S(1,0)SumEntrp, S(1,0)Entropy, S(0,1)SumAverg. Correlation analysis revealed positive associations between the texture features with Node-RADS score: S(4,0)Entropy (r = 0.72, p < 0.001), S(3,0) Entropy (r = 0.72, p < 0.001), S(2,2)Entropy (r = 0.72, p < 0.001). CONCLUSIONS: Several texture features and Node-RADS derived from CT were associated with the malignancy of mediastinal lymph nodes and might therefore be helpful for discrimination purposes. Both of the two quantitative assessments could be translated and used in clinical routine.