Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?

Purpose: To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases. Materials and methods: In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models. Results: Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001). Conclusion: Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.

Real-Life Experience in the Treatment of Intrahepatic Cholangiocarcinoma by 90Y Radioembolization: A Multicenter Retrospective Study.

Limited treatment options in patients with intrahepatic cholangiocarcinoma (iCCA) demand the introduction of new, catheter-based treatment options. Especially, 90Y radioembolization may expand therapeutic abilities beyond surgery or chemotherapy. Therefore, the purpose of this study was to identify factors associated with an improved median overall survival (mOS) in iCCA patients receiving radioembolization in a retrospective study at 5 major tertiary-care centers. Methods: In total, 138 radioembolizations in 128 patients with iCCA (female, 47.7%; male, 52.3%; mean age ± SD, 61.1 ± 13.4 y) were analyzed. Clinical data, imaging characteristics, and radioembolization reports, as well as data from RECIST, version 1.1, analysis performed 3, 6, and 12 mo after radioembolization, were collected. mOS was compared among different subgroups using Kaplan-Meier curves and the log-rank test. Results: Radioembolization was performed as first-line treatment in 25.4%, as second-line treatment in 38.4%, and as salvage treatment in 36.2%. In patients receiving first-line, second-line, and salvage radioembolization, the disease control rate was 68.6%, 52.8%, and 54.0% after 3 mo; 31.4%, 15.1%, and 12.0% after 6 mo; and 17.1%, 5.7%, and 6.0% after 1 y, respectively. In patients receiving radioembolization as first-line, second-line, and salvage treatment, mOS was 12.0 mo (95% CI, 7.6-23.4 mo), 11.8 mo (95% CI, 9.1-16.6 mo), and 8.4 mo (95% CI, 6.3-12.7 mo), respectively. No significant differences among the 3 groups were observed (P = 0.15). Hepatic tumor burden did not significantly influence mOS (P = 0.12). Conclusion: Especially in advanced iCCA, second-line and salvage radioembolization may be important treatment options. In addition to ongoing studies investigating the role of radioembolization as first-line treatment, the role of radioembolization in the later treatment stages of the disease demands further attention.

Adrenal Surgery in the Era of Multidisciplinary Endocrine Tumor Boards.

Work up of adrenal masses includes assessment of endocrine activity and malignancy risk. There is no indication for surgical removal of nonfunctional adrenal adenomas, according to the guidelines. In the present study, we aimed at evaluating the impact of a university endocrine tumor board on the quality of the indications for adrenal surgery at our institution. One hundred consecutive patients receiving primary adrenal surgery at the University Hospital of Cologne, Germany were included. Their demographics, clinic-pathologic characteristics, treatment and outcome were analyzed. In 55 (55%) cases, indication for surgery consisted in functional benign tumors, including Conn, Cushing adenomas and pheochromocytomas. Forty (40%) tumors were referred to surgery for malignancy suspicion and 5 (5%) myelolipomas were removed due to their size. Eighty-nine percent of surgeries were performed as minimally invasive procedures. Overall morbidity included two (2%) self-limiting pancreatic fistulas after left laparoscopic adrenalectomy for pheochromocytoma. All functional tumors were confirmed benign by final histology. Only 33 (82.5%) of 40 suspicious cases turned out to be malignant. Consequently, nonfunctional benign adenomas were "unnecessarily" removed in only 7 (7%) patients, with 6 (85.7%) of them having a history of extra-adrenal cancer and all of them fulfilling criteria for surgery, according to the international guidelines. In conclusion, the endocrine tumor board provided an excellent adherence to the guidelines with most surgeries being performed either for functional or malignant tumors. In nonfunctional tumors with history of extra adrenal cancer, CT guided biopsy might be considered for obviating surgery.

Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation.

PURPOSE: Aim of this study was to develop and evaluate a software toolkit, which allows for a fully automated body composition analysis in contrast enhanced abdominal computed tomography leveraging the strengths of both, quantitative information from dual energy computed tomography and simple detection and segmentation tasks performed by deep convolutional neuronal networks (DCNN). METHODS AND MATERIALS: Both, public and private datasets were used to train and validate DCNN. A combination of two DCNN and quantitative thresholding was used to classify axial CT slices to the abdominal region, classify voxels as fat and muscle and to differentiate between subcutaneous and visceral fat. For validation, patients undergoing repetitive examination (±21 days) and patients who underwent concurrent bioelectrical impedance analysis (BIA) were analyzed. Concordance correlation coefficient (CCC), linear regression and Bland-Altman-Analysis were used as statistical tests. RESULTS: Results provided from the algorithm toolkit were visually validated. The automated classifier was able to extract slices of interest from the full body scans with an accuracy of 98.7 %. DCNN-based segmentation for subcutaneous fat reached a mean dice similarity coefficient of 0.95. CCCs were 0.99 for both muscle and subcutaneous fat and 0.98 for visceral fat in patients undergoing repetitive examinations (n = 48). Further linear regression and Bland-Altman-Analyses suggested good agreement (r2:0.67-0.88) between the software toolkit and patients who underwent concurrent BIA (n = 39). CONCLUSION: We describe a software toolkit allowing for an accurate analysis of body composition utilizing a combination of DCNN- and threshold-based segmentations from spectral detector computed tomography.

The impact of irreversible image data compression on post-processing algorithms in computed tomography.

PURPOSE We aimed to evaluate the influence of irreversible image compression at varying levels on image post-processing algorithms (3D volume rendering of angiographs, computer-assisted detection of lung nodules, segmentation and volumetry of liver lesions, and automated evaluation of functional cardiac imaging) in computed tomography (CT). METHODS Uncompressed CT image data (30 angiographs of the lower limbs, 38 lung exams, 20 liver exams and 30 cardiac exams) were anonymized and subsequently compressed using the JPEG2000 algorithm with compression ratios of 8:1, 10:1, and 15:1. Volume renderings of CT angiographies obtained from compressed and uncompressed data were compared using objective and subjective measures. Computer-assisted detection of lung nodules was performed on compressed and uncompressed image data and compared with respect to diagnostic performance. Segmentation and volumetry of liver lesions as well as measurement of ejection fraction on cardiac studies was performed on compressed and uncompressed datasets; differences in measurements were analyzed. RESULTS No differences could be detected for the 3D volume renderings and no statistically significant differences in performance were found for the computer-assisted detection algorithm. Measurements in volumetry of liver lesions and functional cardiac imaging showed good to excellent reliability. CONCLUSION Irreversible image compression within the limits proposed by the European Society of Radiology has no significant influence on commonly used image post-processing algorithms in CT.