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Background This article aims to develop and assess the radiomics paradigm for predicting colorectal cancer liver metastasis (CRLM) from the primary tumor. Methods This retrospective study included 100 patients from the First Hospital of Jilin University from June 2017 to December 2017. [https://www.selleckchem.com/products/vx-661.html VX661] The 100 patients comprised 50 patients with and 50 without CRLM. The maximum-level enhanced computed tomography (CT) image of primary cancer in the portal venous phase of each patient was selected as the original image data. To automatically implement radiomics-related paradigms, we developed a toolkit called Radiomics Intelligent Analysis Toolkit (RIAT). Results With RIAT, the model based on logistic regression (LR) using both the radiomics and clinical information signatures showed the maximum net benefit. The area under the curve (AUC) value was 0.90±0.02 (sensitivity =0.85±0.02, specificity =0.79±0.04) for the training set, 0.86±0.11 (sensitivity =0.85±0.09, specificity =0.75±0.19) for the verification set, 0.906 (95% CI, 0.840-0.971; sensitivity =0.81, specificity =0.84) for the cross-validation set, and 0.899 (95% CI, 0.761-1.000; sensitivity =0.78, specificity =0.91) for the test set. Conclusions The radiomics nomogram-based LR with clinical risk and radiomics features allows for a more accurate classification of CRLM using CT images with RIAT. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background Bioluminescence imaging (BLI) has been found to have diverse applications in the life sciences and medical research due to its ease of use and high sensitivity. From kinetics analysis, dynamic imaging studies have significant advantages for diagnosis when compared to traditional static imaging studies. This work focuses on modeling and quantitatively analyzing the dynamic data produced from the intraperitoneal (IP) injection of D-luciferin in longitudinal BLI, aiming to provide a powerful tool for monitoring the growth of tumors. Methods We constructed a three-compartment pharmacokinetic (PK) model and employed the standard Michaelis-Menten (M-M) kinetics to investigate the dynamic BLI data produced from the IP injection of D-luciferin. The 3 compartments were the plasma compartment, the non-specific compartment, and the specific compartment. The validity of this PK model was tested by the dynamic BLI data of MKN28M-luc xenograft mice, along with the published longitudinal dynamic BLI data of B16F10-luc xenograft mice. Results The R-squares between the simulated lines and the measurement were 1 and 0.99, respectively, for the mice data and the published data. In addition, the 2 kinetic macroparameters obtained reflected the rate of tumor growth in vivo. In particular, the values of macroparameters A showed a significant dependence on tumor surface area. Conclusions The proposed PK model may be an effective tool for use in drug development programs and for monitoring the response of tumors to treatment. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background We performed a volume analysis of gravity stress (GS) and simulated weight bearing (WB) CBCT scans of a cadaveric supination external rotation (SER) ankle fracture model. Methods An AO supination external rotation 44B3.1 ankle fracture was simulated in 6 human cadavers, each serving as its own control. MCS volume (mm3) was measured on GS and WB CBCT scans. Paired t-tests were used to compare the MCS volume for control versus experimental conditions for GS and WB conditions, and means ± standard deviation are presented. Results MCS on GS CBCT was greater for the experimental (1,540.15±374.8) versus control (984.5±226.5) groups (P=0.004), and MCS on WB CBCT was also greater for the experimental (1,225.57±274.1) versus control (1,059.40±266.6) groups (P=0.05). MCS on GS CBCT was greater for the experimental group compared to both WB CBCT controls (P=0.005) and WB CBCT experimental group (P=0.04). Additionally, MCS on WB CBCT was greater for the experimental group compared to GS CBCT controls (P=0.002), however there was no statistically significant difference in MCS on GS CBCT for controls versus WB CBCT for controls (P=0.08). Conclusions MCS volume increased on WB CBCT scans using a cadaveric SER ankle fracture model. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa). Methods In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 64 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively. Results Both the radiomics model (AUC 0.98) and the clinical-radiomics combined model (AUC 0.98) achieved greater predictive efficacy than the clinical model (AUC 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusions Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.