Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study

BackgroundThis study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT.MethodsIn total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis.ResultsThirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively.ConclusionsThe proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy

Establishment of a chloroplast transformation system inTisochrysis lutea

Tisochrysis luteais a haptophyte microalga commonly used as a commercial feed for juvenile fish and shellfish larvae. Genetic modification is of considerable importance for developing the potential economic value ofT. lutea. However, the genetic transformation system ofT. luteahas not yet been established, which limits functional genomic studies and strain improvement of this important microalgal species. In the current study, a chloroplast transformation vector harboring the phosphinothricin resistance gene (bar) as a selectable marker was established, and two short peptide-encoding genes (ant1 andant2) driven by the endogenouspsbAandrbcLpromoters were cloned in this vector. The recombinant plasmid was transformed using a biolistic method into thetrnI/trnAlocus of the chloroplast genome via homologous recombination. After continuous selection on phosphinothricin, the integration of foreign genes and the expression of specific products in the transformants were detected using polymerase chain reaction (PCR), Southern blotting, and western blot analysis. This is the first report of establishment of a stable transformation system inT. lutea, which is a prerequisite for functional genomics and applied research on this species

Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics

Abstract The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas and schwannomas and underwent preoperative CT examinations. Radiomics features of the entire primary tumor were extracted from non-contrast enhancement (NC), arterial phase (AP) and venous phase (VP) CT images. The least absolute shrinkage and selection operator method was used to screen out key radiomics signatures. Radiomics, clinical and clinical-radiomics combined models were built to differentiate the retroperitoneal paragangliomas and schwannomas. Model performance and clinical usefulness were evaluated by receiver operating characteristic curve, calibration curve and decision curve. In addition, we compared the diagnostic accuracy of radiomics, clinical and clinical-radiomics combined models with radiologists for pheochromocytomas and schwannomas in the same set of data. Three NC, 4 AP, and 3 VP radiomics features were retained as the final radiomics signatures for differentiating the paragangliomas and schwannomas. The CT characteristics CT attenuation value of NC and the enhancement magnitude at AP and VP were found to be significantly different statistically (P < 0.05). The NC, AP, VP, Radiomics and clinical models had encouraging discriminative performance. The clinical-radiomics combined model that combined radiomics signatures and clinical characteristics showed excellent performance, with an area under curve (AUC) values were 0.984 (95% CI 0.952–1.000) in the training cohort, 0.955 (95% CI 0.864–1.000) in the internal validation cohort and 0.871 (95% CI 0.710–1.000) in the external validation cohort. The accuracy, sensitivity and specificity were 0.984, 0.970 and 1.000 in the training cohort, 0.960, 1.000 and 0.917 in the internal validation cohort and 0.917, 0.923 and 0.818 in the external validation cohort, respectively. Additionally, AP, VP, Radiomics, clinical and clinical-radiomics combined models had a higher diagnostic accuracy for pheochromocytomas and schwannomas than the two radiologists. Our study demonstrated the CT-based radiomics models has promising performance in differentiating the paragangliomas and schwannomas

Focal organizing pneumonia in patients: differentiation from solitary bronchioloalveolar carcinoma using dual-energy spectral computed tomography.

ObjectiveTo explore the utility of dual-energy spectral computed tomography (CT) in the differential diagnosis of focal organizing pneumonia (FOP) and solitary bronchioloalveolar carcinoma (S-BAC).Materials and methodsThe institutional review board approved this study and waived the requirement for informed consent. It is a retrospective study. A total of 105 patients (62 with FOP and 43 with S-BAC) enrolled and all patients have contrast enhanced spectral CT including the arterial phase (AP) and venous phase (VP). During AP and VP, CT40 keV, CT70 keV, and CT100 keV values, iodine concentration (IC), water concentration (WC), and effective atomic number (Zeff) were measured on monochromatic and iodine-based material decomposition images, and the slope of the spectral curve (λHu) was calculated. The two-sample t-test was used to compare quantitative parameters, and receiver operating characteristic (ROC) curves were generated to calculate diagnostic efficacies.ResultsFor AP, CT40 keV and CT70 keV values, IC, WC, Zeff, λ70 keV, and λ100 keV measurements, there were significantly higher in patients with S-BAC than in those with FOP (P &lt; 0.05). However, these quantitative parameters of VP were significantly lower in patients with S-BAC than in those with FOP (P &lt; 0.05). ROC curve analysis revealed that the combination of all quantitative parameters in AP and VP provided the best diagnostic performance in distinguishing S-BAC from FOP (area under the ROC curve, 93.1%; sensitivity, 95.3%; specificity, 77.4%).ConclusionsDual-energy spectral CT has the potential to identify S-BAC and FOP

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

BackgroundThis study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.MethodsIn total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.ResultsThere were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation&nbsp;vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].ConclusionOur combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma