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.

Diagnostic efficacy of [68Ga]Ga-DOTA-GPFAPI-04 in patients with solid tumors in a head-to-head comparison with [18F]F-FDG: results from a prospective clinical study.

PURPOSE: To identify the biodistribution and diagnostic performance of a novel fibroblast activation protein (FAP) targeted positron emission tomography (PET) tracer, [68Ga]Ga-DOTA-GPFAPI-04, in patients with solid tumors in a head-to-head comparison with [18F]F-FDG. METHODS: Twenty-six patients histologically proven with cancers of nasopharyngeal (n = 5), esophagus (n = 5), gastro-esophagus (n = 1), stomach (n = 7), liver (n = 3), and colorectum (n = 5) were recruited for [68Ga]Ga-DOTA-GPFAPI-04 and [18F]F-FDG PET/CT scans on consecutive days. The primary endpoint was the diagnostic efficacy, with the histological diagnosis and the follow-up results selected as the gold standard. The secondary endpoint was the background uptake pattern. Two experienced nuclear medicine physicians who were blinded to the gold standard results while having essential awareness of the clinical context reviewed the images and labeled lesions by consensus for subsequent software-assisted lesion segmentation. Additionally, background organs were automatically segmented, assisted by artificial intelligence. Volume, mean, and maximum standard uptake values (SUVmean and SUVmax) of all segmentations were recorded. P < 0.05 was deemed as statistically significant. RESULTS: Significant glandular uptake of [68Ga]Ga-DOTA-GPFAPI-04 was detected in the thyroid, pancreas, and submandibular glands, while moderate uptake was observed in the parotid glands. The myocardium and myometrium exhibited 2-3 times higher uptake of the radiotracer than that of the background levels in blood and liver. A total of 349 targeted lesions, consisting of 324 malignancies and 25 benign lesions, were segmented. [68Ga]Ga-DOTA-GPFAPI-04 is more sensitive than [18F]F-FDG, especially for abdominopelvic dissemination (1.000 vs. 0.475, P < 0.001). Interestingly, [18F]F-FDG demonstrated higher sensitivity for lung metastasis compared to [68Ga]Ga-DOTA-GPFAPI-04 (0.845 vs. 0.682, P = 0.003). The high glandular uptake made it difficult to delineate lesions in close proximity and masked two metastatic lesions in these organs. CONCLUSION: Despite prominent glandular uptake, [68Ga]Ga-DOTA-GPFAPI-04 demonstrates favorable diagnostic performance. It is a promising probe scaffold for further development of FAP-targeted tumor theranostic agents.

A rationally designed nuclei-targeting FAPI 04-based molecular probe with enhanced tumor uptake for PET/CT and fluorescence imaging.

PURPOSE: Fibroblast activation protein inhibitor (FAPI) -based probes have been widely studied in the diagnosis of various malignant tumors with positron emission tomography/computed tomography (PET/CT). However, current imaging studies of FAPI-based probes face challenges in rapid clearance rate and potential false-negative results. Furthermore, FAPI has been rarely explored in optical imaging. Considering this, further modifications are imperative to improve the properties of FAPI-based probes to address existing limitations and broaden their application scenarios. In this study, we rationally introduced methylene blue (MB) to FAPIs, thereby imparting nuclei-targeting and fluorescence imaging capabilities to the probes. Furthermore, we evaluated the added value of FAPI-based fluorescence imaging to traditional PET/CT, exploring the potential application of FAPI-based probes in intraoperative fluorescence imaging. METHODS: A new FAPI-based probe, namely NOTA-FAPI-MB, was designed for both PET/CT and fluorescence imaging by conjugation of MB. The targeting efficacy of the probe was evaluated on fibroblast activation protein (FAP)-transfected cell line and human primary cancer-associated fibroblasts (CAFs). Subsequently, PET/CT and fluorescence imaging were conducted on tumor-bearing mice. The tumor detection and boundary delineation were assessed by fluorescence imaging of tissues from hepatocellular carcinoma (HCC) patients. RESULTS: NOTA-FAPI-MB demonstrated exceptional targeting ability towards FAP-transfected cells and CAFs in comparison to NOTA-FAPI. This benefit arises from the cationic methylene blue (MB) affinity for anionic nucleic acids. PET/CT imaging of tumor-bearing mice revealed significantly higher tumor uptake of [18F]F-NOTA-FAPI-MB (standard uptake value of 2.20 ± 0.31) compared to [18F]F-FDG (standard uptake value of 1.66 ± 0.14). In vivo fluorescence imaging indicated prolonged retention at the tumor site, with retention lasting up to 24 h. In addition, the fluorescent probes enabled more precise lesion detection and tumor margin delineation than clinically used indocyanine green (ICG), achieving a 100.0% (6/6) tumor-positive rate for NOTA-FAPI-MB while 33.3% (2/6) for ICG. These findings highlighted the potential of NOTA-FAPI-MB in guiding intraoperative surgical procedures. CONCLUSIONS: The NOTA-FAPI-MB was successfully synthesized, in which FAPI and MB simultaneously contributed to the targeting effect. Notably, the nuclear delivery mechanism of the probes improved intracellular retention time and targeting efficacy, broadening the imaging time window for fluorescence imaging. In vivo PET/CT demonstrated favorable performance of NOTA-FAPI-MB compared to [18F]F-FDG. This study highlights the significance of fluorescence imaging as an adjunct technique to PET/CT. Furthermore, the encouraging results obtained from the imaging of human HCC tissues hold promise for the potential application of NOTA-FAPI-MB in intraoperative fluorescent surgery guidance within clinical settings.

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.

Kinetic modeling and parametric imaging of 18 F-PSMA-11: An evaluation based on total-body dynamic positron emission tomography scans.

BACKGROUND: The prostate-specific membrane antigen (PSMA) targeted positron-emitting tomography (PET) tracers are increasingly used in clinical practice, with novel tracers constantly being developed. Recently, 18 F-PSMA-11 has been gaining growing interest for several merits; however, direct in vivo visualization of its kinetic features in humans remains lacking. PURPOSE: To visualize the kinetic features of 18 F-PSMA-11 in healthy subjects and patients with prostate cancer derived from the total-body dynamic PET scans. METHODS: A total of 8 healthy volunteers (7 males; 1 female) and 3 patients with prostate cancer underwent total-body PET/CT imaging at 1 and 2 h post injection (p.i.) of 18 F-PSMA-11, of which 7 healthy subjects and 3 patients underwent total-body dynamic PET scans lasting 30 min. Reversible two-tissue compartments (2TC) and Patlak models were fitted based on the voxel-based time activity curves (TACs), with the parametric images generated subsequently. Additionally, semi-automated segmentation of multiple organs was performed in the dynamic images to measure the SUVmean at different time points and in the parametric images to estimate the mean value of the kinetic parameters of these organs. RESULTS: 18 F-PSMA-11 showed quick accumulation within prostate cancer, as early as 45 s after tracer injection. It was rapidly cleared from blood circulation and predominantly excreted through the urinary system. High and rapid radiotracer accumulation was observed in the liver, spleen, lacrimal glands, and salivary glands, whereas gradual accumulation was observed in the skeleton. Prostate cancer tissue is visualized in all parametric images, and best seen in DV and Patlak Ki images. Patlak Ki showed a good correlation with 2TC Ki values (r = 0.858, p < 0.05) but less noise than 2TC images. A scanning time point of 30-35 min p.i. was then suggested for satisfactory tumor to background ratio. CONCLUSION: Prostate cancer tissue is visible in most parametric images, and is better shown by Patlak Ki and 2TC DV images. Patlak Ki is consistent with, and thus is preferred over, 2TC Ki images for substantially quicker calculation. Based on the dynamic imaging analysis, a shorter uptake time (30-35 min) might be preferred for a better balance of tumor to background ratio.

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.

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.

The value of dual-energy spectral CT in differentiating solitary pulmonary tuberculosis and solitary lung adenocarcinoma.

Background: To explore the value of dual-energy spectral CT in distinguishing solitary pulmonary tuberculosis (SP-TB) from solitary lung adenocarcinoma (S-LUAD). Methods: A total of 246 patients confirmed SP-TB (n = 86) or S-LUAD (n = 160) were retrospectively included. Spectral CT parameters include CT40keV value, CT70keV value, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff), and spectral curve slope (λ70keV). Data were measured during the arterial phase (AP) and venous phase (VP). Chi-square test was used to compare categorical variables, Wilcoxon rank-sum test was used to compare continuous variables, and a two-sample t-test was used to compare spectral CT parameters. ROC curves were used to calculate diagnostic efficiency. Results: There were significant differences in spectral CT quantitative parameters (including CT40keV value [all P< 0.001] , CT70keV value [all P< 0.001], λ70keV [P< 0.001, and P = 0.027], Zeff [P =0.015, and P = 0.001], and IC [P =0.002, and P = 0.028]) between the two groups during the AP and VP. However, WC (P = 0.930, and P = 0.823) was not statistically different between the two groups. The ROC curve analysis showed that the AUC in the AP and VP was 90.9% (95% CI, 0.873-0.945) and 83.4% (95% CI, 0.780-0.887), respectively. The highest diagnostic performance (AUC, 97.6%; 95% CI, 0.961-0.991) was achieved when all spectral CT parameters were combined with clinical variables. Conclusion: Dual-energy spectral CT has a significant value in distinguishing SP-TB from S-LUAD.

Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study.

Background: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas. Methods: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination. Results: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1. Conclusion: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.

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.

Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.

Purpose: The aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC). Methods: Up until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis. Results: An aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41-0.58), 0.80 (95% CI, 0.75-0.85), 4 (95% CI, 3-6), and 0.73 (95% CI, 0.69-0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45-0.64), 0.87 (95% CI, 0.81-0.91), 8 (95% CI, 5-12), and 0.80 (95% CI, 0.76-0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively. Conclusion: The results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction.

68Ga-Labeled Cystine Knot Peptide Targeting Integrin αvß6 for Lung Cancer PET Imaging.

Integrin αvß6 has been considered as a promising biomarker for lung cancer, and its expression is often related to poor prognosis. An αvß6-binding cystine knot peptide R01-MG was previously engineered and validated. Here, we developed a positron emission tomography (PET) probe of R01-MG for imaging αvß6-positive lung cancer. Cystine knot peptide R01-MG was synthesized through solid-phase peptide synthesis chemistry and radiolabeled with 68Ga after being conjugated with 1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid (DOTA). The stability of 68Ga-DOTA-R01-MG was analyzed in phosphate-buffered saline (PBS) (pH 7.4) and fetal bovine serum (FBS). The cell uptake assay of the probe was evaluated using αvß6-positive (A549 and H1975) and αvß6-negative (H1299) lung cancer cell lines. In addition, small animal PET imaging and biodistribution studies of 68Ga-DOTA-R01-MG were performed in αvß6-positive and αvß6-negative lung cancer models. Our study showed that 68Ga-DOTA-R01-MG exhibited excellent stability in PBS and FBS. Small animal PET imaging and biodistribution data revealed that 68Ga-DOTA-R01-MG displayed rapid and good tumor uptake in animal models with αvß6-positive lung cancer, and the probe was rapidly cleared from the normal tissues, resulting in good tumor-to-normal tissue contrasts. Meanwhile, no obvious tumor uptake of 68Ga-DOTA-R01-MG was observed in animal models with αvß6-negative lung cancer, demonstrating specific binding of the probe to integrin αvß6. In conclusion, 68Ga-DOTA-R01-MG has great potential to be a promising PET tracer for imaging αvß6-positive lung cancer.

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.

Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia.

OBJECTIVE: To investigate the spectral and perfusion computed tomography (CT) findings of peripheral lung cancer (PLC) and focal organizing pneumonia (FOP) and to compare the accuracy of spectral and perfusion CT imaging in distinguishing PLC from FOP. MATERIALS AND METHODS: Patients who were suspected of having lung tumor and underwent "one-stop" chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. Patients who were suspected of having lung tumor and underwent "one-stop" chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. A total of 57 and 35 patients with PLC and FOP were included, respectively. Spectral parameters (CT40keV, CT70keV, CT100keV, iodine concentration [IC], water concentration [WC], and effective atomic number [Zeff]) of the lesions in the arterial and venous phases were measured in both groups. The slope of the spectral curve (K70keV) was calculated. The perfusion parameters, including blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS), were measured simultaneously in both groups. The differences in the spectral and perfusion parameters between the groups were examined. Receiver operating characteristic (ROC) curves were generated to calculate and compare the area under the curve (AUC), sensitivity, specificity, and accuracy of both sets of parameters in both groups. RESULTS: The patients' demographic and clinical characteristics were similar in both groups (P > 0.05). In the arterial and venous phases, the values of spectral parameters (CT40keV, CT70keV, spectral curve K70keV, IC, and Zeff) were greater in the FOP group than in the PLC group (P < 0.05). In contrast, the values of the perfusion parameters (BV, BF, MTT, and PS) were smaller in the FOP group than in the PLC group (P < 0.05). The AUC of the combination of the spectral parameters was larger than that of the perfusion parameters. For the former imaging method, the AUC, sensitivity, and specificity were 0.89 (95% confidence interval [CI]: 0.82-0.96), 0.86, and 0.83, respectively. For the latter imaging method, the AUC, sensitivity, and specificity were 0.80 (95% CI: 0.70-0.90), 0.71, and 0.83, respectively. There was no significant difference in AUC between the two imaging methods (P > 0.05). CONCLUSION: Spectral and perfusion CT both has the capability to differentiate PLC and FOP. However, compared to perfusion CT imaging, spectral CT imaging has higher diagnostic efficiency in distinguishing 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.

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.