Construction and validation of a clinical differentiation model between peripheral lung cancer and solitary pulmonary tuberculosis.

OBJECTIVE: To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging features. MATERIALS AND METHODS: Retrospectively, 183 patients (100 PLC, 83 SP-TB) in our hospital were randomly divided into a training group and an internal validation group (ratio 7:3), and 100 patients (50 PLC, 50 SP-TB) in Sichuan Provincial People's Hospital were identified as an external validation group. The collected qualitative and quantitative variables were used to determine the independent feature variables for distinguishing between PLC and SP-TB through univariate logistic regression, multivariate logistic regression. Then, traditional logistic regression models and machine learning algorithm models (decision tree, random forest, xgboost, support vector machine, k-nearest neighbors, light gradient boosting machine) were established using the independent feature variables. The model with the highest AUC value in the internal validation group was used for subsequent analysis. The receiver operating characteristic curve (ROC), calibration curve, and decision curves analysis (DCA) were used to assess the model's discrimination, calibration, and clinical usefulness. RESULT: Age, smoking history, maximum diameter of lesion, lobulation, spiculation, calcification, and vascular convergence sign were independent characteristic variables to differentiate PLC from SP-TB. The logistic regression model had the highest AUC value of 0.878 for the internal validation group, based on which a quantitative visualization nomogram was constructed to discriminate the two diseases. The area under the ROC curve (AUC) of the model in the training, internal validation, and external validation groups were 0.915 (95 % CI: 0.866-0.965), 0.878 (95 % CI: 0.784-0.971), and 0.912 (95 % CI: 0.855-0.969), respectively, and the calibration curves fitted well. Decision curves analysis (DCA) confirmed the good clinical benefit of the model. CONCLUSION: The model constructed based on clinical and imaging features can accurately differentiate between PLC and SP-TB, providing potential value for developing reasonable clinical plans.

Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients.

RATIONALE AND OBJECTIVES: This study aimed to non-invasively predict epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase computed tomography (CT) radiomics features. MATERIALS AND METHODS: A total of 424 patients with lung adenocarcinoma were recruited from two hospitals who underwent preoperative non-enhanced CT (NE-CT) and enhanced CT (including arterial phase CT [AP-CT], and venous phase CT [VP-CT]). Patients were divided into training (n = 297) and external validation (n = 127) cohorts according to hospital. Radiomics features were extracted from the NE-CT, AP-CT, and VP-CT images, respectively. The Wilcoxon test, correlation analysis, and simulated annealing were used for feature screening. A clinical model and eight radiomics models were established. Furthermore, a clinical-radiomics model was constructed by incorporating multi-phase CT features and clinical risk factors. Receiver operating characteristic curves were used to evaluate the predictive performance of the models. RESULTS: The predictive performance of multi-phase CT radiomics model (AUC of 0.925 [95% CI, 0.879-0.971] in the validation cohort) was higher than that of NE-CT, AP-CT, VP-CT, and clinical models (AUCs of 0.860 [95% CI,0.794-0.927], 0.792 [95% CI, 0.713-0.871], 0.753 [95% CI, 0.669-0.838], and 0.706 [95% CI, 0.620-0.791] in the validation cohort, respectively) (all P < 0.05). The predictive performance of the clinical-radiomics model (AUC of 0.927 [95% CI, 0.882-0.971] in the validation cohort) was comparable to that of multi-phase CT radiomics model (P > 0.05). CONCLUSION: Our multi-phase CT radiomics model showed good performance in identifying the EGFR mutation status in patients with lung adenocarcinoma, which may assist personalized treatment decisions.

Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study.

BACKGROUND: Hepatic echinococcosis is a severe endemic disease in some underdeveloped rural areas worldwide. Qualified physicians are in short supply in such areas, resulting in low rates of accurate diagnosis of this condition. In this study, we aimed to develop and evaluate an artificial intelligence (AI) system for automated detection and subtyping of hepatic echinococcosis using plain CT images with the goal of providing interpretable assistance to radiologists and clinicians. METHODS: We developed EDAM, an echinococcosis diagnostic AI system, to provide accurate and generalisable CT analysis for distinguishing hepatic echinococcosis from hepatic cysts and normal controls (no liver lesions), as well as subtyping hepatic echinococcosis as alveolar or cystic echinococcosis. EDAM includes a slice-level prediction model for lesion classification and segmentation and a patient-level diagnostic model for patient classification. We collected a plain CT database (n=700: 395 cystic echinococcosis, 122 alveolar echinococcosis, 130 hepatic cysts, and 53 normal controls) for developing EDAM, and two additional independent cohorts (n=156) for external validation of its performance and generalisation ability. We compared the performance of EDAM with 52 experienced radiologists in diagnosing and subtyping hepatic echinococcosis. FINDINGS: EDAM showed reliable performance in patient-level diagnosis on both the internal testing data (overall area under the receiver operating characteristic curve [AUC]: 0·974 [95% CI 0·936-0·994]; accuracy: 0·952 [0·939-0·965] for cystic echinococcosis, 0·981 [0·973-0·989] for alveolar echinococcosis; sensitivity: 0·966 [0·951-0·979] for cystic echinococcosis, 0·944 [0·908-0·970] for alveolar echinococcosis) and the external testing set (overall AUC: 0·953 [95% CI 0·840-0·973]; accuracy: 0·929 [0·915-0·947] for cystic echinococcosis, 0·936 [0·919-0·950] for alveolar echinococcosis; sensitivity: 0·913 [0·879-0·944] for cystic echinococcosis, 0·868 [0·841-0·897] for alveolar echinococcosis). The sensitivity of EDAM was robust across images from different CT manufacturers. EDAM outperformed most of the enrolled radiologists in detecting both alveolar echinococcosis and cystic echinococcosis. INTERPRETATION: EDAM is a clinically applicable AI system that can provide patient-level diagnoses with interpretable results. The accuracy and generalisation ability of EDAM demonstrates its potential for clinical use, especially in underdeveloped areas. FUNDING: Project of Qinghai Provincial Department of Science and Technology of China, National Natural Science Foundation of China, and Tsinghua-Fuzhou Institute of Data Technology Project. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

A radiomics model enables prediction venous sinus invasion in meningioma.

OBJECTIVE: Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging. METHODS: In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. RESULTS: Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively. INTERPRETATION: The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.

Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics.

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.

Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics.

BACKGROUND: In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT. METHODS: This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model. RESULTS: Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status. CONCLUSION: The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.

7T Small Animal MRI Research for Hepatic Alveolar Echinococcosis.

OBJECTIVES: 7T small animal magnetic resonance imaging (MRI) was used to analyze the growth characteristics of hepatic alveolar echinococcosis (HAE). METHODS: A mouse model of HAE was established by intraperitoneal injection of alveolar Echinococcus tissue suspension. Ten mouse models successfully inoculated by ultrasound screening were selected. The mouse model was scanned with T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequence by 7T small animal MRI. Size, morphology, boundary, signal, and relationship with surrounding tissues of the lesions were recorded as characteristic alterations. Mice were killed at the end of the experiment, and the pathological specimens were taken for routine hematoxylin and eosin staining. RESULTS: Lesions were mainly located in the right lobe of the liver. The multivesicular structure is the characteristic manifestation of this disease. In the liver, lesions invaded the portal vein and were mainly distributed at the hepatic hilum. The left branch of the portal vein was mainly invaded. The mean diameter of the lesions in the left lobe of the liver was larger than in other parts of the liver. The mean diameter of the cystic solid lesions was greater than the multilocular cystic lesions. HAE showed hypointense on T1WI, hyperintense on T2WI, and hypointense on DWI; the marginal zone of the lesion showed hyperintensity on DWI and grew toward the hilum. The MRI features of intraperitoneal lesions were similar to those of intrahepatic lesions. Intraperitoneal lesions increased faster than intrahepatic lesions in the same period. CONCLUSION: Polyvesicular structure is a characteristic manifestation of hepatic alveolar echinococcosis in mice. The noninvasive monitoring of liver HAE in mice by 7T small animal MRI provides a visual basis for the diagnosis and treatment integration of HAE.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma.

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

Magnetic resonance imaging study of normal cranial bone marrow conversion at high altitude.

Background: To use conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) to investigate the effects of long-term hypoxia on cranial bone marrow conversion in healthy people at high altitudes. Methods: A total of 1,130 individuals were selected from altitudinal areas of 2,000-3,000, 3,100-4,000, and >4,100 m. Each altitude range was divided into 5 age groups: 0-5, 6-14, 15-29, 30-49, and ≥50 years. Firstly, cranial bone marrow typing of the participants in each altitude range was performed on sagittal T1-weighted images (T1WI) according to the average diploe thickness and signal intensity of the normal skull, and the relationship between bone marrow conversion and age was analyzed. Secondly, the apparent diffusion coefficient (ADC) values of the frontal bone, parietal bone, occipital bone, and temporal bone were measured in the DWI post-processing workstation and statistical methods were used to analyze whether different altitudinal gradients and long-term hypoxic environment had any effect on cranial bone marrow conversion. Results: There was a positive correlation between bone marrow type and age in the healthy populations at all 3 levels of altitude (P<0.05). The average thickness of the cranial diploe also positively correlated with age (P<0.05); in the age ranges of 30-49 and ≥50 years, the ADC values of the occipital and temporal bone marrow positively correlated with increasing altitude (P<0.05). Conclusions: The cranial bone marrow of normal people at high altitudes changes from Type I to Type IV with increasing age and under the influence of long-term chronic hypoxia. The bone marrow of the occipital and temporal bones of healthy people aged 30-49 and ≥50 years showed erythromedularization during the process of Type III and IV bone marrow conversion.

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.

Background: This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features. Methods: In 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. Results: There 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 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]. Conclusion: Our 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.

A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas.

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.

Modified Burr-Hole Craniostomy for the Treatment of Chronic Subdural Hematoma in Adults.

BACKGROUND: Burr-hole craniostomy (BHC) is considered to be the most effective method for the treatment of chronic subdural hematoma (CSDH), and middle meningeal artery embolization is a new therapy used in clinical practice in recent years to treat CSDH. However, the optimal therapeutic effect of these 2 procedures is still controversial. This study prospectively designed a modified burr-hole craniostomy (mBHC) with drainage to treat CSDH. METHODS: A total of 101 patients diagnosed with CSDH from January 2019 to April 2020 were prospectively included in this study. They were divided into BHC and mBHC groups. Among them, 40 selected CSDH patients received mBHC treatment. For comparison, 61 CSDH patients who received BHC treatment were used as the control group. Primary outcomes were hematoma recurrence and postoperative complications. Secondary outcomes included midline recovery, hematoma clearance, operation time, and hospital stay. The Chi-square test was used to compare the 6-month follow-up results between the 2 groups. RESULTS: Among patients treated with mBHC, 39 patients had a good prognosis, and one 87-year-old patient with bilateral hematoma died of postoperative heart failure. Of the patients treated with BHC, 52 patients had good prognoses, and one 53-year-old patient with unilateral hematoma died of postoperative acute intracranial bleeding. During the 6-month follow-up period, no relapse occurred in the patients treated with mBHC, whereas 8 (13%) of the patients treated with BHC relapsed. There was a significant difference in the recurrence rate between the 2 groups (P < 0.05). In addition, midline recovery, hematoma clearance rate, operation time, and complications were found to be significantly different statistically (P < 0.05), and other characteristics of operation and outcome were not significantly different (P > 0.05) between the 2 groups. CONCLUSIONS: Modified burr-hole craniostomy has a positive therapeutic effect on patients with CSDH and is more effective than conventional BHC therapy.

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes.

OBJECTIVE: To develop nomograms that combine clinical characteristics, computed tomographic (CT) features and 18F-fluorodeoxyglucose PET (18F-FDG PET) metabolic parameters for individual prediction of epidermal growth factor receptor (EGFR) mutation status and exon 19 deletion mutation and exon 21 point mutation (21 L858R) subtypes in lung adenocarcinoma. METHODS: In total 124 lung adenocarcinoma patients who underwent EGFR mutation testing and whole-body 18F-FDG PET/CT were enrolled. Each patient's clinical characteristics (age, sex, smoking history, etc.), CT features (size, location, margins, etc.) and four metabolic parameters (SUVmax, SUVmean, MTV and TLG) were recorded and analyzed. Logistic regression analyses were performed to screen for significant predictors of EGFR mutation status and subtypes, and these predictors were presented as easy-to-use nomograms. RESULTS: According to the results of multiple regression analysis, three nomograms for individualized prediction of EGFR mutation status and subtypes were constructed. The area under curve values of three nomograms were 0.852 (95% CI, 0.783-0.920), 0.857 (95% CI, 0.778-0.937) and 0.893 (95% CI, 0.819-0.968) of EGFR mutation vs. wild-type, 19 deletion mutation vs. wild-type and 21 L858R vs. wild-type, respectively. Only calcification showed significant differences between the EGFR 19 deletion and 21 L858R mutations. CONCLUSION: EGFR 21 L858R mutation was more likely to be nonsolid texture with air bronchograms and pleural retraction on CT images. And they were more likely to be associated with lower FDG metabolic activity compared with those wild-types. The sex difference was mainly caused by the 19 deletion mutation, and calcification was more frequent in them.

Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer.

OBJECTIVE: This study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC). MATERIAL AND METHODS: We retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram. RESULTS: The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility. CONCLUSIONS: This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

The Benefit of Apparent Diffusion Coefficient in Evaluating the Invasiveness of Hepatocellular Carcinoma.

BACKGROUND: This study aimed to evaluate hepatocellular carcinoma (HCC) invasiveness using the apparent diffusion coefficient (ADC). METHODS: Eighty-one patients with HCC confirmed by pathology and examined by preoperative magnetic resonance imaging diffusion-weighted imaging from January 2015 to September 2020 were retrospectively analyzed. Clinical and pathological data were recorded. The minimum ADC (ADCmin), average ADC (ADCmean), and the ratio of ADCmean to normal-appearing hepatic parenchyma ADC (ADCnahp) were assessed. The associations between clinical information, ADC value, and HCC invasiveness (microvascular invasion [MVI], tumor differentiation, and Ki-67 expression) were evaluated statistically. Independent risk factors related to HCC invasiveness were screened using binary logistic regression, and the diagnostic efficiency was evaluated by the receiver operating characteristic curve and its area under the curve (AUC) value. RESULTS: Tumor size was related to HCC MVI and tumor differentiation (P < 0.05). HCC MVI was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.860, 0.860, and 0.909, respectively. If these were combined with tumor size, the AUC value increased to 0.912. The degree of tumor differentiation was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.719, 0.708, and 0.797, respectively. If these were combined with tumor size, the AUC value increased to 0.868. Ki-67 expression was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.731, 0.747, and 0.746, respectively. Combined them, the AUC value increased to 0.763. CONCLUSIONS: The findings indicated that the ADC value has significant potential for the non-invasive preoperative evaluation of HCC invasiveness.

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

BACKGROUND: This 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. METHODS: In 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. RESULTS: Thirty-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. CONCLUSIONS: The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.

Development of a dual-energy spectral computed tomography-based nomogram for the preoperative discrimination of histological grade in colorectal adenocarcinoma patients.

BACKGROUND: The usefulness of a dual-energy spectral computed tomography (DESCT)-based nomogram in discriminating between histological grades of colorectal adenocarcinoma (CRAC) is unclear. This study aimed to develop such a nomogram and assess its ability to preoperatively discriminate between histological grades in CRAC patients. METHODS: Primary tumors monochromatic CT value, iodine concentration (IC) value, and effective atomic number (Eff-Z) in the arterial (AP) and venous phases (VP) were retrospectively compared between patients with high-grade (n=65) and low-grade (n=108) CRAC who underwent preoperative abdominal DESCT. Univariate analysis was used to compare the DESCT parameters and clinical factors between these two patient groups. Statistically significant features in the univariate analysis were included in the multivariate logistic regression model to identify the indicators for building a nomogram that could discriminate between histological grades in CRAC patients. The clinical usefulness of the nomogram and its value for predicting overall survival were statistically evaluated. RESULTS: The logistic regression analysis showed that age, clinical T stage, clinical N stage, and IC values in AP and VP were significant independent predictors for high-grade CRAC. A quantitative nomogram developed based on these predictors showed excellent performance for discriminating between the histological grades, with an area under the curve (AUC) of 0.886 and excellent agreement in the calibration curve. The Kaplan-Meier curve for overall survival showed that our nomogram identified a significant difference between the high- and low-risk groups [hazard ratio (HR), 2.188; 95% CI, 1.072-4.465; P=0.027). CONCLUSIONS: This study presents a nomogram that incorporates DESCT parameters and clinical factors and can potentially be used as a clinical tool for individual preoperative prediction of CRAC histological grade.

Predicting EGFR mutation status in lung adenocarcinoma: development and validation of a computed tomography-based radiomics signature.

Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma can benefit from targeted therapy. However, noninvasively determination of EGFR mutation status before targeted therapy remains a challenge. This study constructed a nomogram based on a combination of radiomics features with the clinical and radiological features to predict the EGFR mutation status. The least absolute shrinkage and selection operator (LASSO) and Wilcoxon test were used for feature selection. Decision tree (DT), logistic regression (LR), and support vector machine (SVM) classifiers were used for radiomics model building. Used the clinical and radiological features establish clinical-radiology (C-R) model. The C-R model with the best radiomics model to establish clinical-radiological-radiomics (C-R-R) model. The predictive performance of the model was evaluated by ROC and calibration curves, and the clinical usefulness was assessed by a decision curve analysis. The current study showed that twelve radiomics features were significantly associated with EGFR mutations. The best radiomics signature model was obtained using the SVM classifier. The C-R-R model had the best distinguishing ability for predicting the EGFR mutation status, with an AUC of 0.849 (95% CI, 0.805-0.893) and 0.835 (95% CI, 0.761-0.909) in the development and validation cohorts, respectively. Our study provides a non-invasive C-R-R model that combines CT-based radiomics features with clinical and radiological features, which can provide useful image-based biological information for targeted therapy candidates.

Development of a dual-energy spectral CT based nomogram for the preoperative discrimination of mutated and wild-type KRAS in patients with colorectal cancer.

PURPOSE: To develop a dual-energy spectral CT (DESCT) nomogram for the preoperative identification of KRAS mutation in patients with colorectal cancer (CRC). METHOD: One hundred and twenty-four patients who underwent energy spectrum CT pre-operatively were recruited and split into mutated KRAS group (n = 50) and wild-type KRAS group (n = 74). DESCT parameters, including monochromatic CT value, iodine concentration, water concentration, and effective atomic number were measured independently by two reviewers in the arterial, venous, and delayed phases. Normalized iodine concentration (NIC) and slope k of the spectral HU curve were calculated. Evaluate other imaging features such as ATL/LTL ratio, tumor gross pattern, pericolorectal fat invasion (PFI) was also performed by these reviewers. Independent predictors for KRAS mutation were screened out using logistic regression, and these predictors were presented as a nomogram. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram. RESULTS: The slope k in the arterial phase, effective atomic number in the arterial phase, NIC in the venous phase, ATL/LTL ratio and PFI were significant independent predictors for KRAS mutation. Based on these independent predictors, a quantitative nomogram was developed to predict individual KRAS mutation probability. The nomogram had excellent performance with an AUC of 0.848 and excellent calibration. DCA showed that our nomogram has outstanding clinical utility. CONCLUSIONS: This study demonstrates that a DESCT based nomogram has potential value for individual preoperative identification of KRAS mutation in CRC patients.