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

BackgroundThe 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.MethodsA 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).ResultsThe 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.ConclusionThis 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

Oncogenic state and cell identity combinatorially dictate the susceptibility of cells within glioma development hierarchy to IGF1R targeting

Glioblastoma is the most malignant cancer in the brain and currently incurable. It is urgent to identify effective targets for this lethal disease. Inhibition of such targets should suppress the growth of cancer cells and, ideally also precancerous cells for early prevention, but minimally affect their normal counterparts. Using genetic mouse models with neural stem cells (NSCs) or oligodendrocyte precursor cells (OPCs) as the cells‐of‐origin/mutation, it is shown that the susceptibility of cells within the development hierarchy of glioma to the knockout of insulin‐like growth factor I receptor (IGF1R) is determined not only by their oncogenic states, but also by their cell identities/states. Knockout of IGF1R selectively disrupts the growth of mutant and transformed, but not normal OPCs, or NSCs. The desirable outcome of IGF1R knockout on cell growth requires the mutant cells to commit to the OPC identity regardless of its development hierarchical status. At the molecular level, oncogenic mutations reprogram the cellular network of OPCs and force them to depend more on IGF1R for their growth. A new‐generation brain‐penetrable, orally available IGF1R inhibitor harnessing tumor OPCs in the brain is also developed. The findings reveal the cellular window of IGF1R targeting and establish IGF1R as an effective target for the prevention and treatment of glioblastoma

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

PurposeTo 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 methodsIn 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.ResultsA 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).ConclusionsThe 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

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning

This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed. Secondly, the classification model of DenseNet combined with transfer learning was constructed and fine-tuned, MCN and SCN were classified by 5-fold cross validation, and the proposed classification model was compared with AlexNet, VGG16, ResNet50 and other deep learning models. The classification model in this paper yielded the best recognition effect. The area under the ROC curve (AUC value), accuracy rate, recall rate and precision rate of the test set were 0.989, 0.943, 0.949 and 0.938 respectively. It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models, which can help doctors in clinical diagnosis, and save manpower and material resources. It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors

Serine/threonine protein kinase SpkG is a candidate for high salt resistance in the unicellular cyanobacterium Synechocystis sp. PCC 6803.

BACKGROUND: Seven serine/threonine kinase genes have been predicted in unicellular cyanobacterium Synechocystis sp. PCC6803. SpkA and SpkB were shown to be required for cell motility and SpkE has no kinase activity. There is no report whether the other four STKs are involved in stress-mediated signaling in Synechocystis PCC6803. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we examined differential expression of the other four serine/threonine kinases, SpkC, SpkD, SpkF and SpkG, at seven different stress conditions. The transcriptional level was up-regulated of spkG and down-regulated of spkC under high salt stress condition. Two spk deletion mutants, ΔspkC and ΔspkG, were constructed and their growth characteristic were examined compared to the wild strain. The wild strain and ΔspkC mutant were not affected under high salt stress conditions. In contrast, growth of spkG mutant was completely impaired. To further confirm the function of spkG, we also examined the effect of mutation of spkG on the expression of salt stress-inducible genes. We compared genome-wide patterns of transcription between wild-type Synechocystis sp. PCC6803 and cells with a mutation in the SpkG with DNA microarray analysis. CONCLUSION: In this study, we first study the spkG gene as sensor of high salt signal. We consider that SpkG play essential roles in Synechocystis sp. for sensing the high salt signal directly, rather than mediating signals among other kinases. Our microarray experiment may help select relatively significant genes for further research on mechanisms of signal transduction of Synechocystis sp. PCC6803 under high salt stress

Baseline vessel wall magnetic resonance imaging characteristics associated with in-stent restenosis for intracranial atherosclerotic stenosis

BackgroundImaging factors, specifically baseline plaque features on high-resolution magnetic resonance vessel wall imaging (HR-VWI) that could be associated with in-stent restenosis (ISR), are still unknown. We aimed to investigate the presenting clinical and plaque features on HR-VWI associated with ISR.MethodsSixty-four patients with intracranial stent placement for intracranial atherosclerotic stenosis who had pre- and post-contrast T1-weighted HR-VWI on 3.0T prior to stenting were included in this analysis. Student's t-test, Mann-Whitney U test, χ2 test, or the Cochran-Mantel-Haenszel (CMH) test were used to compare clinical and baseline HR-VWI characteristics of the patients between the ISR and non-ISR groups. Univariable and multivariable logistic analysis were used to test the clinical and imaging factors associated with ISR.ResultsAmong the 64 patients, 9 patients (14.06%) developed ISR during the 2-year follow-up period. Plaque burden (median 0.89 vs 0.92, P=0.04), minimum lumen area (0.009 cm2 vs 0.006 cm2, P=0.04), plaque eccentricity (55.6% vs 89.1%, P＜0.01), enhancement ratio (1.36 vs 0.84, P＜0.01), and enhancement involvement (type 2 represents ≥50% cross-sectional wall involvement; 100% vs 63.6%, P=0.03) all significantly differed between patients with and without ISR. Multivariable analysis revealed that lower frequency of plaque eccentricity (OR 0.18, 95% CI 0.04 to 0.96, P=0.04) and higher enhancement ratio (OR 3.57, 95% CI 1.02 to 12.48, P=0.04) were independently associated with ISR.ConclusionsPreliminary findings showed that ISR was independently associated with plaque concentricity and higher enhancement ratios on pre-stenting HR-VWI for patients with symptomatic intracranial atherosclerotic stenosis

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

Abstract 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