Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma.

OBJECTIVES: To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort. RESULTS: The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report. CONCLUSIONS: The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC. KEY POINTS: • The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction.

Fluorescence molecular tomography (FMT) is a preclinical optical tomographic imaging technique that can trace various physiological and pathological processes at the cellular or even molecular level. Reducing the number of FMT projection views can improve the data acquisition speed, which is significant in applications such as dynamic problems. However, a reduction in the number of projection views will dramatically aggravate the ill-posedness of the FMT inverse problem and lead to significant degradation of the reconstructed images. To deal with this problem, we have proposed a deep-learning-based reconstruction method for sparse-view FMT that only uses four perpendicular projection views and divides the image reconstruction into two stages: image restoration and inverse Radon transform. In the first stage, the projection views of the surface fluorescence are restored to eliminate the blur derived from photon diffusion through a fully convolutional neural network. In the second stage, another convolutional neural network is used to implement the inverse Radon transform between the restored projections from the first stage and the reconstructed transverse slices. Numerical simulation and phantom and mouse experiments are carried out. The results show that the proposed method can effectively deal with the image reconstruction problem of sparse-view FMT.

Image restoration for blurry optical images caused by photon diffusion with deep learning.

Optical macroscopic imaging techniques have shown great significance in the investigations of biomedical issues by revealing structural or functional information of living bodies through the detection of visible or near-infrared light derived from different mechanisms. However, optical macroscopic imaging techniques suffer from poor spatial resolution due to photon diffusion in biological tissues. This dramatically restricts the application of optical imaging techniques in numerous situations. In this paper, an image restoration method based on deep learning is proposed to eliminate the blur caused by photon diffusion in optical macroscopic imaging. Two blurry images captured at orthogonal angles are used as the additional information to ensure the uniqueness of the solution and restore the small targets at deep locations. Then a fully convolutional neural network is proposed to accomplish the image restoration, which consists of three sectors: V-shaped network for central view, V-shaped network for side views, and synthetical path. The two V-shaped networks are concatenated to the synthetical path with skip connections to generate the output image. Simulations as well as phantom and mouse experiments are implemented. Results indicate the effectiveness of the proposed method.

Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke.

PURPOSE: To investigate the role of radiomics features in thrombus age identification and establish a CT-based radiomics model for predicting thrombus age of large vessel occlusion stroke patients. METHODS: We retrospectively reviewed patients with middle cerebral artery occlusion receiving mechanical thrombectomy from July 2020 to March 2022 at our center. The retrieved clots were stained with Hematoxylin and Eosin (H&E) and determined as fresh or older thrombi based on coagulation age. Clot-derived radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression analysis, by which selected radiomics features were integrated into the Rad-score via the corresponding coefficients. The prediction performance of Rad-score in thrombus age was evaluated with the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 104 patients were included in our analysis, with 52 in training and 52 in validation cohort. Older thrombi were characterized with delayed procedure time, worse functional outcome and marginally associated with more attempts of device. We extracted 982 features from NCCT images. Following T test and LASSO analysis in training cohort, six radiomics features were selected, based on which the Rad-score was generated by the linear combination of features. The Rad-score showed satisfactory performance in distinguishing fresh with older thrombi, with the AUC of 0.873 (95 %CI: 0.777-0.956) and 0.773 (95 %CI: 0.636-0.910) in training and validation cohort, respectively. CONCLUSION: This study established and validated a CT-based radiomics model that could accurately differentiate fresh with older thrombi for stroke patients receiving mechanical thrombectomy.

T2 -Weighted MR Imaging-Derived Radiomics for Pretreatment Determination of Therapeutic Response to Glucocorticoid in Patients With Thyroid-Associated Ophthalmopathy: Comparison With Semiquantitative Evaluation.

BACKGROUND: MR imaging has been applied to determine therapeutic response to glucocorticoid (GC) before treatment in thyroid-associated ophthalmopathy (TAO), while the performance was still poor. PURPOSE: To investigate the value of T2 -weighted imaging (T2 WI)-derived radiomics for pretreatment determination of therapeutic response to GC in TAO patients, and compare its diagnostic performance with that of semiquantitative parameters. STUDY TYPE: Retrospective. POPULATION: A total of 110 patients (49 ± 12 years; male/female, n = 48/62; responsive/unresponsive, n = 62/48), divided into training (n = 78) and validation (n = 32) cohorts. FIELD STRENGTH/SEQUENCE: 3.0 T, T2 -weighted fast spin echo. ASSESSMENT: W.C. and H.H. (6 and 10 years of experience, respectively) performed the measurements. Maximum, mean, and minimum signal intensity ratios (SIRs) of extraocular muscle (EOM) bellies were collected to construct a semiquantitative imaging model. Radiomics features from volumes of interest covering EOM bellies were extracted and three machine learning-based (logistic regression [LR]; decision tree [DT]; support vector machine [SVM]) models were built. STATISTICAL TESTS: The diagnostic performances of models were evaluated using receiver operating characteristic curve analyses, and compared using DeLong test. Two-sided P < 0.05 was considered statistically significant. RESULTS: The responsive group showed higher minimum signal intensity ratio (SIRmin ) of EOMs than the unresponsive group (training: 1.46 ± 0.34 vs. 1.18 ± 0.39; validation: 1.44 ± 0.33 vs. 1.19 ± 0.20). In both cohorts, LR-based radiomics model demonstrated good diagnostic performance (area under the curve [AUC] = 0.968, 0.916), followed by DT-based (AUC = 0.933, 0.857) and SVM-based models (AUC = 0.919, 0.855). All three radiomics models outperformed semiquantitative imaging model (SIRmin : AUC = 0.805) in training cohort. In validation cohort, only LR-based radiomics model outperformed that of SIRmin (AUC = 0.745). The nomogram integrating LR-based radiomics signature and disease duration further elevated the diagnostic performance in validation cohort (AUC: 0.952 vs. 0.916, P = 0.063). DATA CONCLUSION: T2 WI-derived radiomics of EOMs, together with disease duration, provides a promising noninvasive approach for determining therapeutic response before GC administration in TAO patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 4.

DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma.

OBJECTIVE: To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC). METHODS: A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared. RESULTS: Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone (p < 0.05). CONCLUSION: The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies. KEY POINTS: • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.

Use of Machine Learning Algorithms to Predict the Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke Patients With an Extended Therapeutic Time Window.

OBJECTIVE: The aim of this study was to evaluate the performance of machine learning (ML) algorithms in predicting the functional outcome of mechanical thrombectomy (MT) outside the 6-hour therapeutic time window in patients with acute ischemic stroke (AIS). METHODS: One hundred seventy-seven consecutive AIS patients with large-vessel occlusion in the anterior circulation who underwent MT in the extended time window were enrolled. Clinical, neuroimaging, and treatment variables that could be obtained quickly in the real-world emergency settings were collected. Four machine learning algorithms (random forests, regularized logistic regression, support vector machine, and naive Bayes) were used to predict good outcomes (modified Rankin Scale scores of 0-2) at 90 days by using (1) only variables at admission and (2) both baseline and treatment variables. The performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. Feature importance was ranked using random forest algorithms. RESULTS: Eighty patients (45.2%) had a favorable 90-day outcome. Machine learning models including baseline clinical and neuroimaging characteristics predicted 90-day modified Rankin Scale with an area under the ROC curve of 0.80-0.81, sensitivity of 0.60-0.71 and specificity of 0.71-0.76. Further inclusion the treatment variables significantly improved the predictive performance (mean area under the ROC curve, 0.89-0.90; sensitivity, 0.77-0.85; specificity, 0.75-0.87). The most important characteristics for predicting 90-day outcomes were age, hypoperfusion intensity ratio at admission, and National Institutes of Health Stroke Scale score at 24 hours after MT. CONCLUSIONS: Machine learning algorithms may facilitate prediction of 90-day functional outcomes in AIS patients with an extended therapeutic time window.

Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma.

OBJECTIVE: To develop a radiomics signature based on dynamic contrast-enhanced (DCE) MR images for preoperative prediction of microvascular invasion (MVI) in patients with mass-forming intrahepatic cholangiocarcinoma (IMCC). METHODS: One hundred twenty-six patients with surgically resected single IMCC (34 MVI-positive and 92 MVI-negative) were enrolled and allocated to training and validation cohorts (7:3 ratio). Findings of clinical characteristics and MR features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training cohort. The prediction performance of radiomics signature was evaluated by receiver operating characteristics curve (ROC) analysis. Internal validation was performed on an independent cohort containing 38 patients. RESULTS: Larger tumor size and higher radiomics score were positively correlated with MVI in both training cohort (p < 0.001, < 0.001, respectively) and validation cohort (p = 0.008, 0.001, respectively). The radiomics signature, consisting of seven wavelet features, showed optimal prediction performance in both training (AUC = 0.873) and validation cohorts (AUC = 0.850). CONCLUSION: A radiomics signature derived from DCE-MRI of the liver can be a reliable imaging biomarker for predicting MVI of IMCC, which could aid in tailoring treatment strategies. KEY POINTS: • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging can be a useful tool to preoperatively predict MVI of IMCC. • Larger tumor size is positively correlated with MVI of IMCC.

Shape-based reconstruction of dynamic fluorescent yield with a level set method.

BACKGROUND: Fluorescence molecular tomography (FMT) is an optical imaging technique that reveals biological processes within small animals through non-invasively reconstructing the distributions of fluorescent agents. The primary problem in FMT with non-stationary fluorescent yield is the increase of the unknown parameters to be reconstructed. In this paper, a method is proposed to reconstruct dynamic fluorescent yield. METHODS: A shape-based reconstruction method that recovers dynamic fluorescent yield with a level set method is proposed for FMT. To reduce the number of unknown parameters, a level set function is introduced to describe the shape of target and a small number of parameters are used to describe the fluorescent yields at different time points. RESULTS: Results of simulations and phantom experiments demonstrate that the proposed method can recover well the dynamic fluorescent yields, shapes and locations of the target. CONCLUSIONS: The proposed method can handle the cases with non-stationary fluorescent yields and recover the fluorescent yields at each projection angle.

Reconstruction of in vivo fluorophore concentration variation with structural priors and smooth penalty.

Reconstruction of fluorophore concentration variation in fluorescence molecular tomography is expected to reveal the metabolic processes of fluorescent biomarkers in vivo. However, the complicated and strong noise within in vivo data inhibits its applications for in vivo cases. A smooth penalty method is presented in this work to suppress the noise. The method is based on a recursive reconstruction scheme which reconstructs the fluorophore concentration variation rates (FCVRs) of two neighboring frames at the same time within an inner iteration. In addition, the performance of the Laplacian-type regularization incorporating structural priors is investigated. Results of simulations suggest that the smooth penalty method almost has no influence on the reconstructed FCVRs when the target curve is smooth, and results of in vivo experiments on mice indicate that the method is capable of suppressing the noise and achieving smooth time courses of fluorescent yield. Results of both the simulations and in vivo experiments demonstrate that the Laplacian-type regularization can improve the image quality.

Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) can visualize biological activities at cellular and molecular levels in vivo, and has been extensively used in drug delivery and tumor detection research of small animals. The ill-posedness of the FMT inverse problem makes it difficult to reconstruct and unmix multiple adjacent fluorescent targets that have different functional features but are labeled with the same fluorochrome. A method based on independent component analysis for multispectral excited FMT was proposed in our previous study. It showed that double fluorescent targets with certain edge-to-edge distance (EED) could be unmixed by the method. In this study, the situation is promoted to unmix multiple adjacent fluorescent targets (i.e., more than two fluorescent targets and EED=0). Phantom experiments on the resolving ability of the proposed algorithm demonstrate that the algorithm performs well in unmixing multiple adjacent fluorescent targets in both lateral and axial directions. And also, we recovered the locational information of each independent fluorescent target and described the variable trends of the corresponding fluorescent targets under the excitation spectrum. This method is capable of unmixing multiple fluorescent targets with small EED but labeled with the same fluorochrome, and may be used in imaging of nonspecific probe targeting and metabolism of drugs.

Direct reconstruction method for time-domain fluorescence molecular lifetime tomography.

For the reconstruction of time-domain fluorescence molecular lifetime tomography, conventional methods based on the Laplace or Fourier transform utilize only part of the information from the measurement data, and rely on the selection of transformation factors. To make the best of all the measurement data, a direct reconstruction algorithm is proposed. The fluorescence yield map is first reconstructed with a full-time gate, and then an objective function for the inverse lifetime tomography (instead of the lifetime) is developed so as to avoid dealing with the singularity of the zero points in the lifetime image. Through simulations and physical phantom experiments, the proposed algorithm is demonstrated to have high localization accuracy for fluorescent targets, high quantification accuracy for fluorescence lifetime, and good contrast between different fluorescence targets.

Fast reconstruction of fluorophore concentration variation based on the derivation of the diffusion equation.

The information of fluorophore concentration variation (FCV) has the potential for drug development and tumor studies, but the reconstruction of FCV is time-consuming in dynamic fluorescence molecular tomography (DFMT). In this paper, a time-efficient reconstruction method for FCV is presented. The system equation of this method is derived from the derivation of the diffusion equation, and its size does not change with the number of frames. The computational time can be significantly reduced by using this method because the images of different frames are reconstructed separately. Simulations and phantom experiments are performed to validate the performance of the proposed method. The results show that compared with the previous method, the proposed method can obtain better results and consumes less computational time with the same number of iterations. In addition, the time consumption in a single iteration of the proposed method increases much slower with the number of frames.

Fast reconstruction of fluorescence molecular tomography via a permissible region extraction strategy.

In order to obtain precise reconstruction results in fluorescence molecular tomography (FMT), large-scale matrix equations would be solved in the inverse problem generally. Thus, much time and memory needs to be consumed. In this paper, a permissible region extraction strategy is proposed to solve this problem. First, a preliminary result is rapidly reconstructed using the weight matrix compressed by principal component analysis or uniform sampling. And then the reconstructed target area in this preliminary result is considered as the a priori permissible region to guide the final reconstruction. Phantom experiments with double fluorescent targets are performed to test the performance of the strategy. The results illustrate that the proposed strategy can significantly accelerate the image reconstruction in FMT almost without quality degradation.

MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma.

PURPOSE: To investigate the potential of radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in preoperatively predicting microvascular invasion (MVI) in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC) before surgery. METHODS: A cohort of 91 patients with histologically confirmed cHCC-CC who underwent preoperative liver DCE-MRI were enrolled and divided into a training cohort (27 MVI-positive and 37 MVI-negative) and a validation cohort (11 MVI-positive and 16 MVI-negative). Clinical characteristics and MR features of the patients were evaluated. Radiomics features were extracted from DCE-MRI, and a radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. Prediction performance of the developed radiomics signature was evaluated by utilizing the receiver operating characteristic (ROC) analysis. RESULTS: Larger tumor size and higher Radscore were associated with the presence of MVI in the training cohort (p = 0.026 and < 0.001, respectively), and theses findings were also confirmed in the validation cohort (p = 0.040 and 0.001, respectively). The developed radiomics signature, composed of 4 stable radiomics features, showed high prediction performance in both the training cohort (AUC = 0.866, 95% CI 0.757-0.938, p < 0.001) and validation cohort (AUC = 0.841, 95% CI 0.650-0.952, p < 0.001). CONCLUSIONS: The radiomics signature developed from DCE-MRI can be a reliable imaging biomarker to preoperatively predict MVI in cHCC-CC.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI.

OBJECTIVE: To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). METHODS: A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. RESULTS: Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. CONCLUSION: A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. ADVANCES IN KNOWLEDGE: A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

Objective: To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). Methods: One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Results: Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. Conclusion: The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma.

OBJECTIVE: We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT). METHOD: Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups. RESULTS: A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds. CONCLUSION: A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.

Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties.

Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficiency of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information on NPs physicochemical properties and tumor genomic profile to predict the delivery efficiency. The correlation coefficients were 0.66, 0.75, and 0.54 for the prediction of maximum delivery efficiency, delivery efficiency at 24 and 168 h postinjection for test sets. The analysis of the feature importance revealed that the tumor genomic mutations and their interaction with NPs properties played important roles in the delivery of NPs. The biological pathways of the NP-delivery-related genes were further explored through gene ontology enrichment analysis. Our work provides a pipeline to predict and explain the delivery efficiency of NPs to heterogeneous tumors and highlights the power of simultaneously using omics data and interpretable machine learning algorithms for discovering interactions between NPs and individual tumors, which is important for the development of personalized precision nanomedicine.

Acute Vertebrobasilar Artery Occlusion with Underlying Atherosclerosis: Balloon Angioplasty Combined with Tirofiban as Initial Salvage Therapy.

OBJECTIVE: The optimal rescue endovascular treatment for patients with intracranial atherosclerotic stenosis in acute vertebrobasilar artery occlusion is not well established. We investigated the safety and efficacy of balloon angioplasty combined with tirofiban as the initial rescue strategy in these patients. METHODS: We retrospectively analyzed the records of 41 patients admitted between January 2014 and September 2022, with vertebrobasilar artery atherosclerotic occlusion. Balloon angioplasty in combination with tirofiban was used as the first-line salvage therapy after the failure of mechanical thrombectomy. The technical success rate, recanalization outcome, procedure-related complications, symptomatic intracranial hemorrhage, and functional outcome at 90 days were reviewed. RESULTS: Recanalization with a modified Thrombolysis in Cerebral Infarction grade of 2b-3 was achieved in 38 of the 41 patients (92.7%). Acute stents were deployed in 5 patients who did not achieve successful reperfusion after balloon angioplasty. Six patients (14.6%, 6/41) underwent stent angioplasty in the stable stage for severe residual stenosis detected on follow-up imaging. There was no procedure-related complication. Hemorrhagic transformation was detected on follow-up imaging in 11 patients (26.8%), while no symptomatic intracranial hemorrhage was recorded. Good functional outcome rate was 31.7% (13/41). CONCLUSIONS: Balloon angioplasty combined with intravenous tirofiban administration is a safe and effective salvage therapy in patients with acute atherosclerotic occlusion of the vertebrobasilar artery.