Citations

Background: Due to lacking evidence on surveillance for gastric cancer (GC), this study aimed to determine the optimal postsurgical surveillance strategy for pathological stage (pStage) II/III GC patients and compare its cost-effectiveness with traditional surveillance strategies. Methods: Prospectively collected data from stage II/III GC patients (n=1661) who underwent upfront surgery at a large-volume tertiary cancer center in China (FJMUUH cohort) between January 2010 and October 2015. For external validation, two independent cohorts were included, which were composed of 380 stage II/III GC patients at an tertiary cancer center in U.S.A (Mayo cohort) between July 1991 and July 2012 and 270 stage II/III GC patients at another tertiary cancer center in China (QUAH cohort) between May 2010 and October 2014. Random forest models were used to predict dynamic recurrence hazards and to construct individual surveillance strategies for stage II/III GC. Cost-effectiveness was assessed by the Markov model. Results: The median follow-up period of the FJMUUH, the Mayo, and QUAH cohorts were 55, 158, and 70 months, respectively. In the FJMUUH cohort, the 5-year recurrence risk was higher in pStage III compared with pStage II GC patients (P<0.001). Our novel individual surveillance strategy achieved optimal cost-effectiveness for pStage II GC patients (ICER =$490/QALY). The most intensive NCCN surveillance guideline was more cost-effective (ICER =$983/QALY) for pStage III GC patients. The external validations confirmed our results. Conclusion: For patients with pStage II GC, individualized risk-based surveillance outperformed the JGCTG and NCCN surveillance guidelines. However, the NCCN surveillance guideline may be more suitable for patients with pStage III GC. Even though our results are limited by the retrospective study design, the authors believe that our findings should be considered when recommending postoperative surveillance for stage II/III GC with upfront surgery in the absence of a randomized clinical trial.

AbstractBackgroundThe accuracy of the eighth AJCC ypTNM staging system on the prognosis of gastric cancer (GC) patients after neoadjuvant therapy (NAT) is controversial. This study aimed to develop and validate a novel staging system using the log odds of positive lymph nodes scheme (LODDS).MethodsA retrospective analysis of 606 GC patients who underwent radical gastrectomy after neoadjuvant therapy was conducted as the development cohort. (Fujian Medical University Affiliated Union Hospital (n = 183), Qinghai University Affiliated Hospital (n = 169), Mayo Clinic (n = 236), Lanzhou University First Hospital (n = 18)). The validation cohort came from the SEER database (n = 1701). A novel ypTLoddsS (ypTLM) staging system was established using the 3‐year overall survival. The predictive performance of two systems was compared. ResultsTwo‐step multivariate Cox regression analysis in both cohorts showed that ypTLM was an independent predictor of overall survival of GC patients after neoadjuvant therapy (HR: 1.57, 95% CI: 1.30–1.88, p < 0.001). In the development cohort, ypTLM had better discrimination ability than ypTNM (C‐index: 0.663 vs 0.633, p < 0.001), better prediction homogeneity (LR: 97.7 vs. 70.9), and better prediction accuracy (BIC: 3067.01 vs 3093.82; NRI: 0.36). In the validation cohort, ypTLM had a better prognostic predictive ability (C‐index: 0.614 vs 0.588, p < 0.001; LR: 11,909.05 vs. 11,975.75; BIC: 13,263.71 vs 13,328.24; NRI: 0.22). The time‐dependent ROC curve shows that the predictive performance of ypTLM is better than ypTNM, and the analysis of the decision curve shows that ypTLM achieved better net benefits. ConclusionA LODDS‐based ypTLM staging system based on multicenter data was established and validated. The predictive performance was superior to the eighth AJCC ypTNM staging system.

PurposeTo improve calibrationless parallel imaging using pre‐learned subspaces of coil sensitivity functions.Theory and MethodsA subspace‐based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.ResultsWith no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state‐of‐the‐art methods including JSENSE, DeepSENSE, P‐LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.ConclusionA subspace‐based method named JSENSE‐Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Background The rapid development and complexity of new x‐ray computed tomography (CT) technologies and the need for evidence‐based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. Aims This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. Materials and Methods The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task‐based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. Results The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. Discussion In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification‐based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). Conclusion We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance‐based criteria for CT imaging.

Single-cell diversity in the brain The cells that make up an organism may all start from one genome, but somatic mutations mean that somewhere along the line of development, an organism's individual cellular genomes diverge. McConnell et al. review the implications and causes of single-cell genomic diversity for brain function. Somatic mutations caused by mobile genetic elements or errors in DNA repair may underlie certain neuropsychiatric disorders. Science , this issue p. eaal1641