A clinical and computed tomography-based nomogram to predict the outcome in patients with anterior circulation large vessel occlusion after endovascular mechanical thrombectomy.

PURPOSE: To explore the positive predictors of the clinical outcome in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion (ACLVO) after endovascular mechanical thrombectomy (EMT) at a 90-day follow-up, and to establish a nomogram model to predict the clinical outcome. MATERIALS AND METHODS: AIS patients with ACLVO detected by multimodal Computed Tomography imaging who underwent EMT were collected. Patients were divided into the favorable and the unfavorable groups according to the 90-day modified Rankin Scale (mRS) score. Univariate and multivariate analyses were performed to investigate predictors of the favorable outcome (mRS of 0-2). A nomogram model for predicting the clinical outcome after EMT was drawn, and the receiver operating characteristic (ROC) curve was used to evaluate its predictive value. RESULTS: Totally 105 patients including 65 patients in the favorable group and 40 in the unfavorable group were enrolled. Multivariate logistic regression analysis showed that admission National Institute of Health Stroke scale (NIHSS) score [0.858 (95% CI 0.778-0.947)], ACLVO at M2 [20.023 (95% CI 2.204-181.907)] and infarct core (IC) volume [0.943 (95% CI 0.917-0.969)] was positively correlated with favorable outcome. The accuracy of the nomogram model in predicting the outcome was 0.923 (95% CI 0.870-0.976), with a cutoff value of 119.6 points. The area under the ROC curve was 0.848 (95% CI 0.780-0.917; sensitivity, 79.7%; specificity, 90.0%). CONCLUSION: A low Admission NIHSS score, ACLVO at M2, and a small IC volume were positive predictors for favorable outcome. The nomogram model may well predict the outcome in AIS patients with ACLVO after EMT.

Cross-Modal Consistency for Single-Modal MR Image Segmentation.

OBJECTIVE: Multi-modal magnetic resonance (MR) image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to obtain multiple modalities for a single patient in clinical applications. To address these issues, a cross-modal consistency framework is proposed for a single-modal MR image segmentation. METHODS: To enable single-modal MR image segmentation in the inference stage, a weighted cross-entropy loss and a pixel-level feature consistency loss are proposed to train the target network with the guidance of the teacher network and the auxiliary network. To fuse dual-modal MR images in the training stage, the cross-modal consistency is measured according to Dice similarity entropy loss and Dice similarity contrastive loss, so as to maximize the prediction similarity of the teacher network and the auxiliary network. To reduce the difference in image contrast between different MR images for the same organs, a contrast alignment network is proposed to align input images with different contrasts to reference images with a good contrast. RESULTS: Comprehensive experiments have been performed on a publicly available prostate dataset and an in-house pancreas dataset to verify the effectiveness of the proposed method. Compared to state-of-the-art methods, the proposed method can achieve better segmentation. CONCLUSION: The proposed image segmentation method can fuse dual-modal MR images in the training stage and only need one-modal MR images in the inference stage. SIGNIFICANCE: The proposed method can be used in routine clinical occasions when only single-modal MR image with variable contrast is available for a patient.

D-A-D type based NIR fluorescence probe for monitoring the cysteine levels in pancreatic cancer cell during ferroptosis.

Cysteine (Cys) as a crucial precursor for intracellular glutathione (GSH) synthesis, plays an important role in the redox regulation in ferroptosis, Therefore, evaluating intracellular Cys levels is worthy to better understand ferroptosis-related physiological process. In this work, we constructed a novel NIR coumarin-derived fluorescent probe (NCDFP-Cys) based on a dual-ICT system, the NCDFP-Cys can show fluorescence turn-on response at 717 nm toward Cys over other amino acids, and possess large Stokes shift (Δλ = 167 nm), low detection limit, hypotoxicity. More significantly, NCDFP-Cys has been utilized to monitor the intracellular Cys fluctuation in pancreatic cancer cells during ferroptosis induced by Erastin and RSL3 respectively, and revealing the difference of Cys levels changes in different activator-triggered ferroptosis pathways.

Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2.

OBJECTIVES: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious, fast-spreading, and insidious. Most patients present with normal findings on lung computed tomography (CT). The current study aimed to develop and validate a tracheal CT radiomics model to predict Omicron variant infection. MATERIALS AND METHODS: In this retrospective study, a radiomics model was developed based on a training set consisting of 157 patients with an Omicron variant infection and 239 healthy controls between 1 January and 30 April 2022. A set of morphological expansions, with dilations of 1, 3, 5, 7, and 9 voxels, was applied to the trachea, and radiomic features were extracted from different dilation voxels of the trachea. Logistic regression (LR), support vector machines (SVM), and random forests (RF) were developed and evaluated; the models were validated on 67 patients with the Omicron variant and on 103 healthy controls between 1 May and 30 July 2022. RESULTS: Logistic regression with 12 radiomic features extracted from the tracheal wall with dilation of 5 voxels achieved the highest classification performance compared with the other models. The LR model achieved an area under the curve of 0.993 (95% confidence interval [CI]: 0.987-0.998) in the training set and 0.989 (95% CI: 0.979-0.999) in the validation set. Sensitivity, specificity, and accuracy of the model for the training set were 0.994, 0.946, and 0.965, respectively, whereas those for the validation set were 0.970, 0.952, and 0.959, respectively. CONCLUSION: The tracheal CT radiomics model reliably identified the Omicron variant of SARS-CoV­2, and may help in clinical decision-making in future, especially in cases of normal lung CT findings.

Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-resolution CT images was generated and annotated. We proposed a novel 3D segmentation model named nnTransfer (nonisomorphic transfer learning) net, which employs generative model structure for self-supervision to facilitate the network's learning of image attributes from unlabelled data. The effectiveness for pancreas segmentation of nnTransfer was assessed using the Hausdorff distance (HD) and Dice similarity coefficient (DSC) on the dataset. Additionally, a histogram analysis with local thresholding was used to achieve automated whole-volume measurement of pancreatic fat (fat volume fraction, FVF). The proposed technique performed admirably on the dataset, with DSC: 0.937 ± 0.019 and HD: 2.655 ± 1.479. The mean pancreas volume and FVF of the pancreas were 91.95 ± 23.90 cm3 and 12.67 % ± 9.84 %, respectively. The nnTransfer functioned flawlessly and autonomously, facilitating the use of the FVF to evaluate pancreatic disease, particularly in patients with diabetes.

Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.

Background: Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Materials and methods: Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. Results: The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). Conclusion: The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registration unique identifying number UIN: Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling.

Background: The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models. Methods: A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, three types of DL-models were constructed: Model 1 trained on the total training dataset, Model 2 trained on the rectal-filling dataset, and Model 3 trained on the non-filling dataset. The diagnostic values were evaluated and compared using receiver operating characteristic (ROC) curve analysis, confusion matrix, net reclassification index (NRI), and decision curve analysis (DCA). Results: The automatic segmentation showed excellent performance. The rectal-filling dataset exhibited superior results in terms of DSC and ASD (p = 0.006 and 0.017). The DL-models demonstrated significantly superior classification performance to the subjective evaluation in predicting T-stages for all test datasets (all p < 0.05). Among the models, Model 1 showcased the highest overall performance, with an area under the curve (AUC) of 0.958 and an accuracy of 0.962 in the filling test dataset. Conclusion: This study highlighted the utility of DL-based automatic segmentation and classification models for preoperative T-stage assessment of RC on T2WI, particularly in the rectal-filling dataset. Compared with subjective evaluation, the models exhibited superior performance, suggesting their noticeable potential for enhancing clinical diagnosis and treatment practices.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Colorectal cancer cell membrane biomimetic ferroferric oxide nanomaterials for homologous bio-imaging and chemotherapy application.

The research of nanomaterials for bio-imaging and theranostic are very active nowadays with unprecedented advantages in nanomedicine. Homologous targeting and bio-imaging greatly improve the ability of targeted drug delivery and enhance active targeting and treatment ability of nanomedicine for the tumor. In this work, lycorine hydrochloride (LH) and magnetic iron oxide nanoparticles coated with a colorectal cancer (CRC) cell membrane (LH-Fe3O4@M) were prepared, for homologous targeting, magnetic resonance imaging (MRI), and chemotherapy. Results showed that the LH-Fe3O4@M and Fe3O4@M intensity at HT29 tumor was significantly higher than that Fe3O4@PEG, proving the superior selectivity of cancer cell membrane-camouflaged nanomedicine for homologous tumors and the MRI effect of darkening contrast enhancement were remarkable at HT29 tumor. The LH-Fe3O4@M exhibited excellent chemotherapy effect in CRC models as well as LH alone and achieved a high tumor ablation rate but no damage to normal tissues and cells. Therefore, our biomimetic system achieved a homologous targeting, bio-imaging, and efficient therapeutic effect of CRC.

Variabilities in apparent diffusion coefficient (ADC) measurements of the spleen and the paraspinal muscle: A single center large cohort study.

Purpose: Evaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADCspleen) and the paraspinal muscles (ADCmuscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI). Methods: Two MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm2). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADCspleen and the bilateral ADCmuscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed. Results: The ADCspleen was more scattered and variable than the ADCmuscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADCspleen (ICC, 0.47; CV, 0.070) and ADCmuscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were -0.1 (-25.7%-25.6%) and -0.3 (-8.8%-8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADCspleen (ICC, 0.72; CV, 0.061) and ADCmuscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (-20.0%-24.2%) and 0.7 (-10.0%-11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADCmuscle measurements were lower than those of the ADCspleen both in the repeatability and the comparability analyses (all p < 0.001). Conclusion: Paraspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.

Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study.

BACKGROUND: To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases. METHODS: Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model. RESULTS: The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts. CONCLUSIONS: This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.

Characteristics of culprit intracranial plaque without substantial stenosis in ischemic stroke using three-dimensional high-resolution vessel wall magnetic resonance imaging.

Background and aims: We aim to analyze the difference in quantitative features between culprit and non-culprit intracranial plaque without substantial stenosis using three-dimensional high-resolution vessel wall MRI (3D hr-vw-MRI). Methods: The patients with cerebral ischemic symptoms of the unilateral anterior circulation were recruited who had non-stenotic intracranial atherosclerosis (<50%) confirmed by computed tomographic angiographic (CTA) or magnetic resonance angiography (MRA). All patients underwent 3D hr-vw MRI within 1 month after symptom onset. 3D hr-vw-MRI characteristics, including wall thickness, plaque burden, enhancement ratio, plaque volume and intraplaque hemorrhage, and histogram features were analyzed based on T2-, precontrast T1-, and post-contrast T1-weighted images. Univariate and multivariate logistic regression analysis were used to identify key determinates differentiating culprit and non-culprit plaques and to calculate the odds ratios (ORs) with 95% confidence intervals (CIs). Results: A total of 150 plaques were identified, of which 133 plaques (97 culprit and 36 non-culprit) were in the middle cerebral artery, three plaques (all culprit) were in the anterior cerebral artery (ACA) and 14 (11 culprit and three non-culprit) were in the internal carotid artery (ICA). Of all the quantitative parameters analyzed, plaque volume, maximum wall thickness, minimum wall thickness, plaque burden, enhancement ratio, coefficient of variation of the most stenotic site, enhancement ratio of whole culprit plaque in culprit plaques were significantly higher than those in non-culprit plaques. Multivariate logistic regression analysis found that plaque volume [OR, 1.527 (95% CI, 1.231-1.894); P < 0.001] and enhancement ratio of whole plaque [OR, 1.095 (95% CI, 1.021-1.175); P = 0.011] were significantly associated with culprit plaque. The combination of the two features obtained a better diagnostic efficacy for culprit plaque with sensitivity and specificity (0.910 and 0.897, respectively) than each of the two parameters alone. Conclusion: 3D hr-vw MRI features of intracranial atherosclerotic plaques provided potential values over prediction of ischemic stroke patients with non-stenotic arteries. The plaque volume and enhancement ratio of whole plaque of stenosis site were found to be effective predictive parameters.

Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma.

PURPOSE: To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models' performances were determined from their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. RESULTS: Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47 years; 251 men) and PASC (age, 61.99 ± 9.82 years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model. CONCLUSION: Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC.

Replacing secretin-enhanced MRCP with MRI radiomics model based on a fully automated pancreas segmentation for assessing pancreatic exocrine function in chronic pancreatitis.

OBJECTIVES: To develop and validate a radiomics nomogram based on a fully automated pancreas segmentation to assess pancreatic exocrine function. Furthermore, we aimed to compare the performance of the radiomics nomogram with the pancreatic flow output rate (PFR) and conclude on the replacement of secretin-enhanced magnetic resonance cholangiopancreatography (S-MRCP) by the radiomics nomogram for pancreatic exocrine function assessment. METHODS: All participants underwent S-MRCP between April 2011 and December 2014 in this retrospective study. PFR was quantified using S-MRCP. Participants were divided into normal and pancreatic exocrine insufficiency (PEI) groups using the cut-off of 200 µg/L of fecal elastase-1. Two prediction models were developed including the clinical and non-enhanced T1-weighted imaging radiomics model. A multivariate logistic regression analysis was conducted to develop the prediction models. The models' performances were determined based on their discrimination, calibration, and clinical utility. RESULTS: A total of 159 participants (mean age [Formula: see text] standard deviation, 45 years [Formula: see text] 14;119 men) included 85 normal and 74 PEI. All the participants were divided into a training set comprising 119 consecutive patients and an independent validation set comprising 40 consecutive patients. The radiomics score was an independent risk factor for PEI (odds ratio = 11.69; p < 0.001). In the validation set, the radiomics nomogram exhibited the highest performance (AUC, 0.92) in PEI prediction, whereas the clinical nomogram and PFR had AUCs of 0.79 and 0.78, respectively. CONCLUSION: The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed pancreatic flow output rate on S-MRCP in patients with chronic pancreatitis. KEY POINTS: • The clinical nomogram exhibited moderate performance in diagnosing pancreatic exocrine insufficiency. • The radiomics score was an independent risk factor for pancreatic exocrine insufficiency, and every point rise in the rad-score was associated with an 11.69-fold increase in pancreatic exocrine insufficiency risk. • The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed the clinical model and pancreatic flow output rate quantified by secretin-enhanced magnetic resonance cholangiopancreatography on MRI in patients with chronic pancreatitis.

Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma.

Purpose: To evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Materials and methods: In this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. Results: A total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28-86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36-84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74). Conclusions: The radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.

Predictive value of modified MRI-based split scar sign (mrSSS) score for pathological complete response after neoadjuvant chemoradiotherapy for patients with rectal cancer.

PURPOSE: To measure the diagnostic performance of modified MRI-based split scar sign (mrSSS) score for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for patients with rectal cancer. METHODS: The modified MRI-based split scar sign (mrSSS) score, which consists of T2-weighted images (T2WI)-based score and diffusion-weighted images (DWI)-based score. The sensitivity, specificity, and accuracy of modified mrSSS score, endoscopic gross type, and MRI-based tumor regression grading (mrTRG) score, in the prediction of pCR, were compared. The prognostic value of the modified mrSSS score was also studied. RESULTS: A total of 189 patients were included in the study. The Kendall's coefficient of interobserver concordance of modified mrSSS score, T2WI -based score, and DWI-based score were 0.899, 0.890, and 0.789 respectively. And the maximum and minimum k value of the modified mrSSS score was 0.797 (0.742-0.853) and 0.562 (0.490-0.634). The sensitivity, specificity, and accuracy of prediction of pCR were 0.66, 0.97, and 0.90 for modified mrSSS score; 0.37, 0.89, and 0.78 for endoscopic gross type (scar); and 0.24, 0.92, and 0.77 for mrTRG score (mrTRG = 1). The modified mrSSS score had significantly higher sensitivity than the endoscopic gross type and the mrTRG score in predicting pCR. Patients with lower modified mrSSS scores had significantly longer disease-free survival (P < 0.05). CONCLUSION: The modified mrSSS score showed satisfactory interobserver agreement and higher sensitivity in predicting pCR after nCRT in patients with rectal cancer. The modified mrSSS score is also a predictor of disease-free survival.

Tumor size measurements of pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as CT?

OBJECTIVES: To compare tumor size measurements using CT and MRI in pancreatic cancer (PC) patients with neoadjuvant therapy (NAT). METHODS: This study included 125 histologically confirmed PC patients who underwent NAT. The tumor sizes from CT and MRI before and after NAT were compared by using Bland-Altman analyses and intraclass correlation coefficients (ICCs). Variations in tumor size estimates between MRI and CT in relationship to different factors, including NAT methods (chemotherapy, chemoradiotherapy), tumor locations (head/neck, body/tail), tumor regression grade (TRG) levels (0-2, 3), N stages (N0, N1/N2) and tumor resection margin status (R0, R1), were further analysed. The McNemar test was used to compare the efficacy of NAT evaluations based on the CT and MRI measurements according to RECIST 1.1 criteria. RESULTS: There was no significant difference between the median tumor sizes from CT and MRI before and after NAT (P = 0.44 and 0.39, respectively). There was excellent agreement in tumor size between MRI and CT, with mean size differences and limits of agreement (LOAs) of 1.5 [-9.6 to 12.7] mm and 0.9 [-12.6 to 14.5] mm before NAT (ICC, 0.93) and after NAT (ICC, 0.91), respectively. For all the investigated factors, there was good or excellent correlation (ICC, 0.76 to 0.95) for tumor sizes between CT and MRI. There was no significant difference in the efficacy evaluation of NAT between CT and MRI measurements (P = 1.0). CONCLUSION: MRI and CT have similar performance in assessing PC tumor size before and after NAT.

Semi-supervised interactive fusion network for MR image segmentation.

BACKGROUND: Medical image segmentation is an important task in the diagnosis and treatment of cancers. The low contrast and highly flexible anatomical structure make it challenging to accurately segment the organs or lesions. PURPOSE: To improve the segmentation accuracy of the organs or lesions in magnetic resonance (MR) images, which can be useful in clinical diagnosis and treatment of cancers. METHODS: First, a selective feature interaction (SFI) module is designed to selectively extract the similar features of the sequence images based on the similarity interaction. Second, a multi-scale guided feature reconstruction (MGFR) module is designed to reconstruct low-level semantic features and focus on small targets and the edges of the pancreas. Third, to reduce manual annotation of large amounts of data, a semi-supervised training method is also proposed. Uncertainty estimation is used to further improve the segmentation accuracy. RESULTS: Three hundred ninety-five 3D MR images from 395 patients with pancreatic cancer, 259 3D MR images from 259 patients with brain tumors, and four-fold cross-validation strategy are used to evaluate the proposed method. Compared to state-of-the-art deep learning segmentation networks, the proposed method can achieve better segmentation of pancreas or tumors in MR images. CONCLUSIONS: SFI-Net can fuse dual sequence MR images for abnormal pancreas or tumor segmentation. The proposed semi-supervised strategy can further improve the performance of SFI-Net.