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.

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.

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.

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.

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.

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.

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 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.

Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy.

OBJECTIVE: To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). RESULTS: Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. CONCLUSION: This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. ADVANCES IN KNOWLEDGE: Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.

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.

Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction.

Bioluminescence tomography (BLT) is a promising pre-clinical imaging technique for a wide variety of biomedical applications, which can non-invasively reveal functional activities inside living animal bodies through the detection of visible or near-infrared light produced by bioluminescent reactions. Recently, reconstruction approaches based on deep learning have shown great potential in optical tomography modalities. However, these reports only generate data with stationary patterns of constant target number, shape, and size. The neural networks trained by these data sets are difficult to reconstruct the patterns outside the data sets. This will tremendously restrict the applications of deep learning in optical tomography reconstruction. To address this problem, a self-training strategy is proposed for BLT reconstruction in this paper. The proposed strategy can fast generate large-scale BLT data sets with random target numbers, shapes, and sizes through an algorithm named random seed growth algorithm and the neural network is automatically self-trained. In addition, the proposed strategy uses the neural network to build a map between photon densities on surface and inside the imaged object rather than an end-to-end neural network that directly infers the distribution of sources from the photon density on surface. The map of photon density is further converted into the distribution of sources through the multiplication with stiffness matrix. Simulation, phantom, and mouse studies are carried out. Results show the availability of the proposed self-training strategy.

Multiparametric magnetic resonance imaging for differentiating active from inactive thyroid-associated ophthalmopathy: Added value from magnetization transfer imaging.

PURPOSE: To investigate the feasibility of using magnetization transfer (MT) magnetic resonance imaging for evaluating patients with thyroid-associated ophthalmopathy (TAO), and determine its added value for differentiating active from inactive TAO and predicting clinical activity score (CAS), compared with conventional fat-saturated T2-weighted and diffusion-weighted imaging. METHODS: Orbital MT, fat-saturated T2-weighted, and diffusion-weighted imaging of 60 prospectively enrolled consecutive patients with TAO was analyzed. Simplified histogram parameters (mean, max, min) of magnetization transfer ratio (MTR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) at extraocular muscles were calculated for each orbit and compared between the active and inactive groups. RESULTS: Intraclass correlation coefficients of MTRs and SIRs were similar (0.802-0.963 vs 0.812-0.974, respectively), followed by those of ADCs (0.714-0.855). Patients with active TAO showed significantly lower MTRs and higher SIRs and ADCs than those with inactive TAO (P < 0.05). MTRmean achieved the highest area under the curve (AUC) of 0.868 for differentiating active from inactive group, followed by SIRmax (AUC, 0.836). MTRmean also demonstrated a higher and negative correlation with CAS (r = -0.614, P < 0.001) than MTRmax and MTRmin (r = -0.495, P < 0.001; r = -0.243, P = 0.007; respectively). Support vector machine-based analysis revealed that uniting MTRs could prosper concurrently added performance for disease activity differentiation and CAS prediction, compared with merely combining SIRs and ADCs (AUC, 0.933 vs 0.901; r = 0.703 vs. 0.673). CONCLUSIONS: MT imaging could potentially be used as a noninvasive method for differentiating the activity of TAO and predicting CAS, thereby offering added value to conventional SIR and ADC.

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.

ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps.

BACKGROUND: Acute ischemic stroke is one of the leading death causes. Delineating stoke infarct core in medical images plays a critical role in optimal stroke treatment selection. However, accurate estimation of infarct core still remains challenging because of 1) the large shape and location variation of infarct cores; 2) the complex relationships between perfusion parameters and final tissue outcome. METHODS: We develop an encoder-decoder based semantic model, i.e., Ischemic Stroke Prediction Network (ISP-Net), to predict infarct core after thrombolysis treatment on CT perfusion (CTP) maps. Features of native CTP, CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume), MTT (Mean Transit Time), Tmax are generated and fused with five-path convolutions for comprehensive analysis. A multi-scale atrous convolution (MSAC) block is firstly put forward as the enriched high-level feature extractor in ISP-Net to improve prediction accuracy. A retrospective dataset which is collected from multiple stroke centers is used to evaluate the performance of ISP-Net. The gold standard infarct cores are delineated on the follow-up scans, i.e., non-contrast CT (NCCT) or MRI diffusion-weighted image (DWI). RESULTS: In clinical dataset cross-validation, we achieve mean Dice Similarity Coefficient (DSC) of 0.801, precision of 81.3%, sensitivity of 79.5%, specificity of 99.5%, Area Under Curve (AUC) of 0.721. Our approach yields better outcomes than several advanced deep learning methods, i.e., Deeplab V3, U-Net++, CE-Net, X-Net and Non-local U-Net, demonstrating the promising performance in infarct core prediction. No significant difference of the prediction error is shown for the patients with follow-up NCCT and follow-up DWI (P >0.05). CONCLUSION: This study provides an approach for fast and accurate stroke infarct core estimation. We anticipate the prediction results of ISP-Net could offer assistance to the physicians in the thrombolysis or thrombectomy therapy selection.

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.

Microstructural changes of white matter assessed with diffusional kurtosis imaging in extremely preterm infants with severe intraventricular hemorrhage.

Objective: Intraventricular hemorrhage (IVH) is a serious neurological complication in premature infants. This study aimed to investigate the white matter impairments and neurodevelopmental outcomes of severe IVH in extremely preterm infants with gestation age less than 28 weeks. Methods: We retrospectively evaluated the extremely preterm infants between 2017 and 2020. Neurodevelopmental outcomes were evaluated with the Bayley Scales of Infant and Toddler Development-III at 2 years of corrected age. Diffusional kurtosis imaging (DKI) was employed to evaluate the microstructural changes in white matter tracts. Mean kurtosis (MK) and fractional anisotropy (FA) values of DKI were measured in the brain regions including posterior limbs of the internal capsule (PLIC) and the corpus callosum at term equivalent age. Results: Of 32 extremely preterm infants with severe IVH during the follow-up period, 18 cases were identified as neurodevelopmental impairments. The delay rates of motor and language were 58.4% and 52.7%. The cases with neurodevelopmental impairments had lower MK and FA values in both bilateral PLIC and the corpus callosum. The analysis of multivariable regression models predicting motor and language outcomes at 2 years of corrected age, showed that the decreases of MK values in both PLIC and the corpus callosum at the term equivalent age contributed to a significantly increased risk of neurodevelopmental impairments (all p < 0.05). During follow-up period, obvious loss of nerve fiber bundles was observed with DKI tractography. Conclusion: Motor and language abilities at age 2 years were associated with MK values of DKI at the term equivalent age in both PLIC and the corpus callosum of extremely preterm infants with severe IVH. The evaluation of white matter microstructural changes with MK values might provide feasible indicators of neurodevelopmental outcomes of extremely preterm infants with severe intraventricular hemorrhage.

GANDA: A deep generative adversarial network conditionally generates intratumoral nanoparticles distribution pixels-to-pixels.

Intratumoral nanoparticles (NPs) distribution is critical for the success of nanomedicine in imaging and treatment, but computational models to describe the NPs distribution remain unavailable due to the complex tumor-nano interactions. Here, we develop a Generative Adversarial Network for Distribution Analysis (GANDA) to describe and conditionally generates the intratumoral quantum dots (QDs) distribution after i.v. injection. This deep generative model is trained automatically by 27,775 patches of tumor vessels and cell nuclei decomposed from whole-slide images of 4 T1 breast cancer sections. The GANDA model can conditionally generate images of intratumoral QDs distribution under the constraint of given tumor vessels and cell nuclei channels with the same spatial resolution (pixels-to-pixels), minimal loss (mean squared error, MSE = 1.871) and excellent reliability (intraclass correlation, ICC = 0.94). Quantitative analysis of QDs extravasation distance (ICC = 0.95) and subarea distribution (ICC = 0.99) is allowed on the generated images without knowing the real QDs distribution. We believe this deep generative model may provide opportunities to investigate how influencing factors affect NPs distribution in individual tumors and guide nanomedicine optimization for molecular imaging and personalized treatment.

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.