Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma.

PURPOSE: To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). MATERIALS AND METHODS: A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. RESULTS: In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807-0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). CONCLUSIONS: The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

Plinabulin plus docetaxel versus docetaxel in patients with non-small-cell lung cancer after disease progression on platinum-based regimen (DUBLIN-3): a phase 3, international, multicentre, single-blind, parallel group, randomised controlled trial.

BACKGROUND: There is an unmet need for second-line and third-line treatments that are effective and tolerable for advanced or metastatic non-small-cell lung cancer (NSCLC) with no driver mutations. METHODS: In this phase 3, international, multicentre, single-blind, parallel group, randomised controlled trial, we enrolled patients from 58 medical centres in Australia, China, and the USA. Eligible patients were adults with epidermal growth factor receptor (EGFR) wild-type NSCLC who had progressed after first-line platinum-based therapy. Patients were randomly assigned (1:1) using an independent stratified randomisation schedule with a block size of four to receive intravenous docetaxel 75 mg/m2 on day 1 and either plinabulin (30 mg/m2) or placebo on days 1 and 8 in 21-day cycles until progression, unacceptable toxic effects, withdrawal, or death. The primary endpoint was overall survival (OS) in the intention-to-treat (ITT) population. Safety was analysed in all patients who had received at least one dose of study drug or placebo. This trial is registered with ClinicalTrials.gov (NCT02504489) and is now closed. FINDINGS: Between Nov 30, 2015, and Jan 6, 2021, 919 patients were screened for inclusion. 360 patients were excluded, and 559 were enrolled and randomly assigned to receive either docetaxel and plinabulin (n=278) or docetaxel and placebo (n=281). 406 (73%) of 559 patients were male, 153 (27%) were female, and 488 (87%) were Asian. Median OS was 10·5 months (95% CI 9·34-11·87) in the plinabulin group compared with 9·4 months (8·38-10·68) in the control group (stratified HR 0·82, 95% CI 0·68-0·99; p=0·0399). Mean OS was 15·08 months (13·42-16·74) in the plinabulin group compared with 12·77 months (11·45-14·10) in the placebo group using restricted mean survival time analysis (difference 2·31 months, 95% CI 0·18-4·44; p=0·0332). Treatment-emergent adverse events occurred in 273 (>99%) of 274 patients in the plinabulin group and 276 (99%) of 278 patients in the control group. Grade 3 or 4 gastrointestinal disorders occurred more frequently in the plinabulin group than in the placebo group, with the most frequent being diarrhoea (24 [9%] of 274 patients vs three [1%] of 278) and vomiting (six [2%] vs one [<1%]), as did transient grade 3 hypertension (50 [18%] vs eight [3%]). Treatment-emergent death was reported in 12 patients (4%) in the plinabulin group and ten patients (4%) in the placebo group. INTERPRETATION: Plinabulin plus docetaxel significantly improved OS as second-line and third-line treatment in patients with advanced or metastatic EGFR wild-type NSCLC and could be considered as a new treatment option in this population. FUNDING: BeyondSpring Pharmaceuticals.

Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma.

Purpose: The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model. Methods: In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models' performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility. Results: Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664-0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781-0.906) and 0.836 (95% CI: 0.732-0.940) in the training and testing cohorts, respectively. Conclusion: The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.

Efficacy and Safety of Biosimilar SCT510 Compared with Bevacizumab for the First-Line Treatment of Advanced Non-Squamous Non-Small Cell Lung Cancer: A Randomized, Double-Blind, Phase III Study.

INTRODUCTION: SCT510 is a biosimilar to bevacizumab (Avastin) reference product (RP) that is approved for various metastatic cancers. In this study, we aimed to demonstrate the equivalence of SCT510 and bevacizumab in terms of efficacy, safety, immunogenicity and pharmacokinetics (PK) in patients with advanced non-squamous non-small cell lung cancer (NSCLC). METHODS: Patients with non-squamous NSCLC were randomized equally to the SCT510 group (comprising SCT510, paclitaxel, and carboplatin) and the bevacizumab group (comprising bevacizumab, paclitaxel, and carboplatin) for 4-6 cycles, followed by maintenance monotherapy with SCT510. The primary endpoint was the objective response rate (ORR) at week 12. Secondary endpoints included 18-week ORR, disease control rate (DCR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and 1-year survival rate, as well as assessments of safety, immunogenicity, and multi-dose PK analysis. RESULTS: Between March 29, 2019, and April 27, 2021, 989 patients were screened and 567 eligible patients were randomly assigned to the SCT510 group (285 patients) and the bevacizumab group (282 patients). The ORR at week 12 was 52.6% [95% confidence interval (CI) 46.66-58.55%] in the SCT510 group and 52.5% (95% CI 46.47-58.47%) in the bevacizumab group. The ORR at week 18 was 55.4% (95% CI 49.46-61.30%) for SCT510 and 55.7% (95% CI 49.68-61.62%) for bevacizumab. The ORR risk ratio (RR) at weeks 12 and 18 was 0.99 (90% CI 0.873-1.133) and 0.99 (90% CI 0.872-1.114), respectively, both within the pre-specified equivalence margin of 0.75-1.33. There were no differences between the two groups in relation to other secondary endpoints, specifically DCR, DOR, PFS, OS, and 1-year survival rate. The overall safety findings were similar between the two treatment groups, and both SCT510 and bevacizumab RP exhibited low immunogenicity. CONCLUSIONS: SCT510 is similar to bevacizumab in clinical efficacy, safety, immunogenicity, and PK in patients with advanced non-squamous NSCLC. The totality of the evidence supports the clinical equivalence of SCT510 and bevacizumab. TRIAL REGISTRATION: NCT03792074.

Improving the cycling stability of lithium metal anodes through separator modification with nano-molybdenum powder.

The deposition of lithium ions in an uneven manner can lead to the formation of lithium dendrites, which can puncture the battery separator and cause a short circuit. Additionally, intermediate polysulfides can shuttle between the separator, resulting in damage to the capacity of lithium-sulfur batteries and corrosion of the negative electrode. It is crucial to address these issues promptly. Hence, we developed a modified separator using nano-molybdenum powder, which effectively inhibits the growth of lithium dendrites and suppresses the shuttle effect of polysulfides. The modified separator extends the lifetime of the lithium metal anode, and the uniform pore distribution of the molybdenum powder facilitates the uniform diffusion of Li+ ions, thereby slowing down the detrimental effects. As a result, Li-S cells equipped with the nano-molybdenum powder modified separator achieve a remarkable capacity of 802 mA h g-1 at a current density of 0.5C and maintain a capacity of up to 614 mA h g-1 after 200 cycles.

Gd-EOB-DTPA enhanced MRI nomogram model to differentiate hepatocellular carcinoma and focal nodular hyperplasia both showing iso- or hyperintensity in the hepatobiliary phase.

BACKGROUND: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP). METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups. RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups. CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneurysm segmentation and detection on CT angiography (CTA) images and to compare its performance with radiology reports. Materials and Methods Consecutive head or head and neck CTA images of suspected unruptured cerebral aneurysms were gathered retrospectively from eight hospitals between February 2018 and October 2021 for model development. An external test set with reference standard digital subtraction angiography (DSA) scans was obtained retrospectively from one of the eight hospitals between February 2022 and February 2023. Radiologists (reference standard) assessed aneurysm segmentation, while model performance was evaluated using the Dice similarity coefficient (DSC). The model's aneurysm detection performance was assessed by sensitivity and comparing areas under the receiver operating characteristic curves (AUCs) between the model and radiology reports in the DSA data set with use of the DeLong test. Results Images from 6060 patients (mean age, 56 years ± 12 [SD]; 3375 [55.7%] female) were included for model development (training: 4342; validation: 1086; and internal test set: 632). Another 118 patients (mean age, 59 years ± 14; 79 [66.9%] female) were included in an external test set to evaluate performance based on DSA. The model achieved a DSC of 0.87 for aneurysm segmentation performance in the internal test set. Using DSA, the model achieved 85.7% (108 of 126 aneurysms [95% CI: 78.1, 90.1]) sensitivity in detecting aneurysms on per-vessel analysis, with no evidence of a difference versus radiology reports (AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.91 [95% CI: 0.87, 0.94]; P = .67). Model processing time from reconstruction to detection was 1.76 minutes ± 0.32 per scan. Conclusion The proposed DL model could accurately segment and detect cerebral aneurysms at CTA with no evidence of a significant difference in diagnostic performance compared with radiology reports. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Payabvash in this issue.

Zinc oxide nanoparticles intercalated with porous carbon as a separator coating for improving the stability of lithium metal anodes.

Metal lithium negative electrodes are considered the "holy grail" of lithium battery negative electrodes due to their ultra-high energy density and low overpotential. However, the arbitrary growth of lithium dendrites during the cycling process hindered its industrialization process. We prepared porous carbon doped with zinc oxide nanoparticles (ZNC-MOF-5) by high-temperature carbonization of MOF-5, and coated ZNC-MOF-5 on the surface of commercial membranes (ZNC-MOF-5@PP). Used to improve the cycling stability of metal lithium negative electrodes. Zinc oxide nanoparticles in ZNC-MOF-5 have good lithium affinity and can promote Li+ deposition. The porous structure with a high specific surface area endows the electrode with high lithium loading capacity, reduces local current density, and obtains a dendrite-free metal lithium negative electrode. The electrochemical cycling performance of Li/Cu batteries indicates that, ZNC-MOF-5@PP. The separator can prevent the growth of dendrites and improve cycling stability, proving that ZNC-MOF-5 can effectively guide the deposition of Li and solve dendrite problems.

Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion.

PURPOSE: To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke. METHODS: We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test. RESULTS: Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05). CONCLUSION: Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

Central nervous system efficacy of aumolertinib versus gefitinib in patients with untreated, EGFR-mutated, advanced non-small cell lung cancer: data from a randomized phase III trial (AENEAS).

BACKGROUND: The initial randomized, double-blinded, actively controlled, phase III ANEAS study (NCT03849768) demonstrated that aumolertinib showed superior efficacy relative to gefitinib as first-line therapy in epidermal growth factor receptor (EGFR)-mutated advanced non-small cell lung cancer (NSCLC). Metastatic disease in the central nervous system (CNS) remains a challenge in the management of NSCLC. This study aimed to compare the efficacy of aumolertinib versus gefitinib among patients with baseline CNS metastases in the ANEAS study. METHODS: Eligible patients were enrolled and randomly assigned in a 1:1 ratio to orally receive either aumolertinib or gefitinib in a double-blinded fashion. Patients with asymptomatic, stable CNS metastases were included. Follow-up imaging of the same modality as the initial CNS imaging was performed every 6 weeks for 15 months, then every 12 weeks. CNS response was assessed by a neuroradiological blinded, independent central review (neuroradiological-BICR). The primary endpoint for this subgroup analysis was CNS progression-free survival (PFS). RESULTS: Of the 429 patients enrolled and randomized in the ANEAS study, 106 patients were found to have CNS metastases (CNS Full Analysis Set, cFAS) at baseline by neuroradiological-BICR, and 60 of them had CNS target lesions (CNS Evaluable for Response, cEFR). Treatment with aumolertinib significantly prolonged median CNS PFS compared with gefitinib in both cFAS (29.0 vs. 8.3 months; hazard ratio [HR] = 0.31; 95% confidence interval [CI], 0.17-0.56; P < 0.001) and cEFR (29.0 vs. 8.3 months; HR = 0.26; 95% CI, 0.11-0.57; P < 0.001). The confirmed CNS overall response rate in cEFR was 85.7% and 75.0% in patients treated with aumolertinib and gefitinib, respectively. Competing risk analysis showed that the estimated probability of CNS progression without prior non-CNS progression or death was consistently lower with aumolertinib than with gefitinib in patients with and without CNS metastases at baseline. No new safety findings were observed. CONCLUSIONS: These results indicate a potential advantage of aumolertinib over gefitinib in terms of CNS PFS and the risk of CNS progression in patients with EGFR-mutated advanced NSCLC with baseline CNS metastases. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT03849768.

The efficacy of postoperative radiotherapy in resected pâ ¢A-N2 EGFR mutant and wild-type lung adenocarcinoma.

The resected pⅢA-N2 non-small-cell lung cancer (NSCLC) patients who could benefit from postoperative radiotherapy (PORT) are not well-defined. The study explored the role of PORT on EGFR mutant and wild-type NSCLC patients. We retrospectively searched for resected pIIIA-N2 lung adenocarcinoma patients who underwent EGFR mutation testing. 80 patients with EGFR wild-type and 85 patients with EGFR mutation were included. 62 patients received PORT. In overall population, the median disease-free survival (DFS) was improved in PORT arm compared to non-PORT arm (22.9 vs. 16.1 months; p = 0.036), along with higher 2-year locoregional recurrence-free survival (LRFS) rate (88.3% vs. 69.3%; p = 0.004). In EGFR wild-type patients, PORT was associated with a longer median DFS (23.3 vs. 17.2 months; p = 0.044), and a higher 2-year LRFS rate (86.8% vs. 61.9%; p = 0.012). In EGFR mutant patients, PORT was not significantly correlated with improved survival outcomes. EGFR wild-type may a biomarker to identify the cohort that benefits from PORT.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.

OBJECTIVE: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). MATERIALS AND METHODS: A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. RESULTS: The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). CONCLUSION: Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. CLINICAL RELEVANCE STATEMENT: The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. KEY POINTS: Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

The Value of Radiomics Features Based on HRCT in Predicting whether the Lung Sub-Centimeter Pure Ground Glass Nodule is Benign or Malignant.

OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Adaptive orthogonal directional total variation with kernel regression for CT image denoising.

BACKGROUND: Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. OBJECTIVE: In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression. METHODS: The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation. RESULTS: Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance. CONCLUSIONS: The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.

Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.

OBJECTIVES: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL). METHODS: This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts. RESULTS: The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency. CONCLUSION: The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention. CLINICAL RELEVANCE STATEMENT: The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications. KEY POINTS: The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

OBJECTIVE: To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). MATERIALS AND METHODS: The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. RESULTS: Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. CONCLUSION: Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Neural Progenitor Cell-Mediated Magnetic Nanoparticles for Magnetic Resonance Imaging and Photothermal Therapy of Glioma.

Glioma is the most common primary malignant tumor in the brain. The diagnostic accuracy and treatment efficiency of glioma are facing great challenges due to the presence of the blood-brain barrier (BBB) and the high infiltration of glioma. There is an urgent need to explore the combination of diagnostic and therapeutic approaches to achieve a more accurate diagnosis, as well as guidance before and after surgery. In this work, we induced human induction of pluripotent stem cell into neural progenitor cells (NPCs) and synthesized nanoprobes labeled with enhanced green fluorescent protein (EGFP, abbreviated as MFe3O4-labeled EGFP-NPCs) for photothermal therapy. Nanoprobes carried by NPCs can effectively penetrate the BBB and target glioma for the purpose of magnetic resonance imaging and guiding surgery. More importantly, MFe3O4-labeled EGFP-NPCs can effectively induce local photothermal therapy, conduct preoperative tumor therapy, and inhibit the recurrence of postoperative glioma. This work shows that MFe3O4-labeled EGFP-NPCs is a promising nanoplatform for glioma diagnosis, accurate imaging-guided surgery, and effective photothermal therapy.

A 32-Month Follow-Up Study of the Effect of APOE ε4 on the Whole Brain Connection in Young Healthy Individuals.

APOE ε4 is risk for cognitive decline even in normal aging, but its effect on the whole-brain functional connectivity (FC) among time in young adults remain elusive. This study aimed to validate the time-by-APOE ε4 interaction on brain FC of this specific population. Longitudinal changes in neuropsychological assessments and resting-state functional magnetic resonance imaging in 26 ε4 carriers and 26 matched non-ε4 carriers were measured for about 3 years. Whole-brain FC was calculated, and a full factorial design was used to compare the difference among groups. Two-sample t test was used for post-hoc analysis. Pearson's correlation analysis was conducted to investigate the relationships between FC and cognitive tests. Of 26 specially appointed ROIs, left superior temporal gyrus (TG) was most sensitive to the effect of time-by-gene interaction. Specifically, the alteration of FC was distributed between the left TG and right TG with GRF correction (voxel-P < 0.001, cluster-P < 0.05), and decreased in ε4 carriers while increased in non-ε4. The main effect of gene showed ε4 carriers has lower FC between left TG and right middle frontal gyrus as compared with non-ε4 both at baseline and follow-up study; ε4 carriers has lower FC between left TG and right supramarginal as compared with non-ε4 at baseline, but no difference in follow-up study. The time-by-APOE ε4 interaction on brain FC was demonstrated at a young age, and left TG was the earliest affected brain regions. The young adult ε4 carriers experience decreased FC among time in the absence overt clinical symptoms.