To increase the temporal resolution and maximal imaging time of super-resolution (SR) microscopy, we have developed a deconvolution algorithm for structured illumination microscopy based on Hessian matrixes (Hessian-SIM). It uses the continuity of biological structures in multiple dimensions as a priori knowledge to guide image reconstruction and attains artifact-minimized SR images with less than 10% of the photon dose used by conventional SIM while substantially outperforming current algorithms at low signal intensities. Hessian-SIM enables rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts and with a spatiotemporal resolution of 88 nm and 188 Hz. Its high sensitivity allows the use of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins, enabling hour-long time-lapse SR imaging of actin filaments in live cells. Finally, we observed the structural dynamics of mitochondrial cristae and structures that, to our knowledge, have not been observed previously, such as enlarged fusion pores during vesicle exocytosis.

Gene fusions prevalent in prostate cancer (CaP) lead to the elevated expression of the ERG proto-oncogene. ERG activation present in 50–70% of prostate tumors underscores one of the most common oncogenic alterations in CaP. Despite numerous reports of gene fusions and mRNA expression, ERG oncoprotein status in CaP still remains to be defined. Furthermore, development of ERG protein-based assays may provide a new dimension to evaluation of gene fusions involving diverse androgen-regulated promoters and the ERG protein-coding sequence. Through exhaustive evaluations of 132 whole-mount prostates (261 tumor foci and over 200 000 benign glands) for the ERG oncoprotein nuclear expression, we demonstrated 99.9% specificity for detecting prostate tumor cells using a highly specific anti-ERG monoclonal antibody. The ERG oncoprotein expression correlated well with fusion transcript or gene fusion in randomly selected specimens. Strong concordance of ERG-positive foci of prostatic intraepithelial neoplasia (PIN) with ERG-positive carcinoma (82 out of 85 sections with PIN, 96.5%) affirms the biological role of ERG in clonal selection of prostate tumors in 65% (86 out of 132) of patients. Conversely, ERG negative PINs were associated with ERG-negative carcinoma. Taken together, the homogeneous and strong ERG expression detected in individual tumors establishes the potential for ERG oncoprotein-based stratification of CaP.

Purpose To extract and study comprehensive spatial–temporal 18F-FDG PET features for the prediction of pathologic tumor response to neoadjuvant chemoradiotherapy (CRT) in esophageal cancer. Methods and Materials Twenty patients with esophageal cancer were treated with trimodality therapy (CRT plus surgery) and underwent FDG PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The two scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold of standardized uptake value (SUV) ≥ 2.5, followed by manual editing. Comprehensive features were extracted to characterize the SUV intensity distribution, spatial patterns (texture), tumor geometry, and associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC). Results The best traditional response measure was maximum SUV (SUVmax) decline (AUC 0.76). Two new intensity features (SUVmean decline and skewness) and three texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUCs ≥ 0.76. According to these features, a tumor was more likely a responder when the mean SUV decline was larger, when there were relatively fewer voxels with higher SUVs pre-CRT, or when FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessments alone. Conclusion Spatial–temporal FDG PET features were found to be useful predictors of pathologic tumor response to neoadjuvant chemoradiotherapy in esophageal cancer. Key words: FDG PET/CT, Tumor response, Esophageal cancer, Quantitative image analysis

The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.