Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade ...gliomas (LGGs).
Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression.
Interrater analysis showed significant agreement in all imaging features scored (k = 0.703-1.000). On multivariate Cox regression analysis, no enhancement and a smooth non-enhancing margin were associated with longer progression-free survival (PFS), while a smooth non-enhancing margin was associated with longer overall survival (OS) after taking into account age, grade, tumor location, histology, extent of resection, and IDH1 1p/19q subtype. Using logistic regression and bootstrap testing evaluations, texture models were found to possess higher prediction potential for IDH1 mutation, 1p/19q codeletion status, histological grade, and progression of LGGs than VASARI features, with areas under the receiver-operating characteristic curves of 0.86 ± 0.01, 0.96 ± 0.01, 0.86 ± 0.01, and 0.80 ± 0.01, respectively.
No enhancement and a smooth non-enhancing margin on MRI were predictive of longer PFS, while a smooth non-enhancing margin was a significant predictor of longer OS in LGGs. Textural analyses of MR imaging data predicted IDH1 mutation, 1p/19q codeletion, histological grade, and tumor progression with high accuracy.
Isocitrate dehydrogenase (
) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the
status of gliomas from MR imaging by applying a ...residual convolutional neural network to preoperative radiographic data.
Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.
With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.
We developed a deep learning technique to noninvasively predict
genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set.
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Pediatric pancreatitis has received much attention during the past few years. Numerous reports have identified an increasing trend in the diagnosis of acute pancreatitis in children and key ...differences in disease presentation and management between infants and older children. The present review provides a brief, evidence-based focus on the latest progress in the clinical field. It also poses important questions for emerging multicenter registries to answer about the natural history and management of affected children with pancreatitis.
•Discriminative Error Prediction Network for Semi-supervised Colon Gland Segmentation•Zhenxi Zhang, Chunna Tian, Harrison X.Bai, Zhicheng Jiao, Xilan Tian•Proposed a novel label rectification method ...ECLR and a semi-supervised segmentation framework ECGSSL.•Proposed a collaborative multi-task discriminative error prediction network DEP-Net•Proposed specific mask degradation methods to highlight the inter-class error and intra-class error•Proposed a dual error correction method for more reliable self-training with the unlabeled part.
Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation directly affects the accuracy on the test set and the subsequent self-training with the error-corrected pseudo labels. In this paper, we propose a novel label rectification method based on error correction, namely ECLR, which can be directly added after the fully-supervised segmentation framework. Moreover, it can be used to guide the semi-supervised learning (SSL) process, constituting an error correction guided SSL framework, called ECGSSL. Specifically, we analyze the types and causes of segmentation error, and divide it into intra-class error and inter-class error caused by intra-class inconsistency and inter-class similarity problems in segmentation, respectively. Further, we propose a collaborative multi-task discriminative error prediction network (DEP-Net) to highlight two error types. For better training of DEP-Net, we propose specific mask degradation methods representing typical segmentation errors. Under the fully-supervised regime, the pre-trained DEP-Net is used to directly rectify the initial segmentation results of the test set. While, under the semi-supervised regime, a dual error correction method is proposed for unlabeled data to obtain more reliable network re-training. Our method is easy to apply to different segmentation models. Extensive experiments on gland segmentation verify that ECLR yields substantial improvements based on initial segmentation predictions. ECGSSL shows consistent improvements over a supervised baseline learned only from labeled data and achieves competitive performance compared with other popular semi-supervised methods.
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be ...strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
To compare the overall survival (OS) outcomes of sublobar resection (SLR) with stereotactic body radiation therapy (SBRT) or ablation for patients with early stage non–small cell lung cancer (NSCLC).
...Patients with clinical stage I (T1-T2aN0M0) NSCLC from 2004 to 2014 who were treated with SLR, SBRT, or ablation as the sole treatment were identified from the National Cancer Database. OS was estimated using the Kaplan–Meier method and evaluated by log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score–matched analysis. Relative survival analyses compared with age- and sex-matched US population were performed.
A total of 53,973 patients were identified. The 1-, 2-, 3-, and 5-year relative survival rates were 96%, 90%, 84%, and 71% for SLR (n = 30,451); 93%, 78%, 65%, and 46% for SBRT (n = 22,134); and 90%, 73%, 58%, and 37% for ablation (n = 1388). Propensity score matching resulted in 9967 patients in the SBRT group versus 9967 in the SLR group and 1062 patients in the ablation group versus 1984 in the SLR group. After matching, both SBRT (hazard ratio, 1.559; 95% confidence interval, 1.497-1.623; P < .001) and ablation (hazard ratio, 1.906; 95% confidence interval, 1.730-2.101; P < .001) were associated with shorter OS when compared with SLR. These results persisted in patients with tumor size ≤2 cm.
Preliminary results suggest SLR may be associated with longer OS in patients with early-stage NSCLC compared with SBRT or ablation. Future prospective, randomized, controlled clinical trials comparing these treatments are needed to confirm these results.
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Segmentation of liver from CT scans is essential in computer-aided liver disease diagnosis and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from numerous learnable ...parameters and high computational cost. In order to overcome this limitation, we propose an Attentive Context-Enhanced Network (AC-E Network) consisting of 1) an attentive context encoding module (ACEM) that can be integrated into the 2D backbone to extract 3D context without a sharp increase in the number of learnable parameters; 2) a dual segmentation branch including complemental loss making the network attend to both the liver region and boundary so that getting the segmented liver surface with high accuracy. Extensive experiments on the LiTS and the 3D-IRCADb datasets demonstrate that our method outperforms existing approaches and is competitive to the state-of-the-art 2D-3D hybrid method on the equilibrium of the segmentation precision and the number of model parameters.
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