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Background: Osimertinib could effectively target epidermal growth factor receptor (EGFR) T790M resistance mutations in non‐small cell lung cancer (NSCLC), indicating that rebiopsy may be particularly important. However, the clinical benefit of repeat rebiopsy in T790M‐negative patients with NSCLC detected by the first rebiopsy is still unclear, and data on the efficacy and safety of osimertinib in patients with NSCLC who are T790M‐positive patients on a repeat rebiopsy remain rare. Methods: We retrospectively collected the clinical data of advanced NSCLC patients with common EGFR mutation who were treated with 1/2‐generation (1/2G) EGFR–tyrosine kinase inhibitors (TKIs) in first‐line therapy in our center from January 2018 to December 2020. The detection rate of T790M by first and repeat rebiopsy was recorded, and we also analyzed the efficacy and safety of osimertinib for T790M‐positive patients. Results: Among 190 common EGFR‐mutant patients who received 1/2G EGFR–TKIs with advanced NSCLC in the first‐line treatment, 141 patients developed progressive disease. In total, 110 of 141 accepted the first rebiopsy, with a T790M prevalence of 50.9% (56/110). In total, 43 T790M‐positive patients who received osimertinib were included in first rebiopsy group. Of 54 T790M‐negative patients detected by the first rebiopsy, 28 underwent repeated rebiopsy in subsequent clinical treatment, and 10 (35.7%) T790M‐positive cases were confirmed. In total, eight T790M‐positive patients treated with osimertinib were included in repeat rebiopsy group. Overall, 66 (60%) of 110 patients acquired a T790M mutation. In patients with the T790M mutation discovered by the first and repeat rebiopsy, osimertinib resulted in median progression‐free survival of 7 (95% confidence interval [CI]: 5.3–8.7) and 6 (95% CI: 4.7–7.3) months, respectively (p =.656). The median overall survival since osimertinib initiation for T790M‐positive patients at first rebiopsy was 20 (95% CI: 15.1–24.9) months and 19 (95% CI: 16.9–21.1) months, for those at repeated rebiopsy (p =.888). Adverse events of grade 3 or higher were similar in the two groups (25.6% vs. 12.5%, p =.616). There was no treatment‐related death in the two groups. Conclusions: Repeat rebiopsy can increase the T790M mutation positivity rate. Osimertinib showed similar efficacy and safety in T790M‐positive patients whether detected by the first or repeat rebiopsy. [ABSTRACT FROM AUTHOR]

Exopolysaccharides (EPSs), which are produced by lactic acid bacteria, have been found to improve the texture and functionality of fermented dairy products. In a previous study, four nitrogen sources were identified as affecting the yield, molecular weight and structure of EPSs produced by Streptococcus thermophilus IMAU20561 in M17 medium. In this genomic and transcriptomics study, a novel eps gene cluster responsible for assembly of repeating units of EPS is reported. This eps cluster (22.3 kb), consisting of 24 open reading frames, is located in the chromosomal DNA. To explore the biosynthetic mechanisms in EPS, we completed RNA-seq analysis of S. thermophilus IMAU20561 grown in four different nitrogen sources for 5 h (log phase) or 10 h (stationary phase). GO functional annotation showed that there was a significant enrichment of differentially expressed genes (DEGs) involved in: amino acid biosynthesis and metabolism; ribonucleotide biosynthesis and metabolism; IMP biosynthesis and metabolism; and phosphorus metabolism. KEGG functional annotation also indicated enrichment of DEGs involved in amino acid biosynthesis, glycolysis, phosphotransferase system, fructose, and mannose metabolism. Our findings provide a better understanding the genetic traits of S. thermophilus , the biosynthetic pathways needed for the production of EPS, and a theoretical basis for screening dairy starter cultures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Peng, Liu, Wu, He, Cui and Dan.)

Purpose: Deep learning can be used to automatically digitize interstitial needles in high-dose-rate (HDR) brachytherapy for patients with cervical cancer. The aim of this study was to design a novel attention-gated deep-learning model, which may further improve the accuracy of and better differentiate needles., Methods and Materials: Seventeen patients with cervical cancer with 56 computed tomography-based interstitial HDR brachytherapy plans from the local hospital were retrospectively chosen with the local institutional review board's approval. Among them, 50 plans were randomly selected as the training set and the rest as the validation set. Spatial and channel attention gates (AGs) were added to 3-dimensional convolutional neural networks (CNNs) to highlight needle features and suppress irrelevant regions; this was supposed to facilitate convergence and improve accuracy of automatic needle digitization. Subsequently, the automatically digitized needles were exported to the Oncentra treatment planning system (Elekta Solutions AB, Stockholm, Sweden) for dose evaluation. The geometric and dosimetric accuracy of automatic needle digitization was compared among 3 methods: (1) clinically approved plans with manual needle digitization (ground truth); (2) the conventional deep-learning (CNN) method; and (3) the attention-added deep-learning (CNN + AG) method, in terms of the Dice similarity coefficient (DSC), tip and shaft positioning errors, dose distribution in the high-risk clinical target volume (HR-CTV), organs at risk, and so on., Results: The attention-gated CNN model was superior to CNN without AGs, with a greater DSC (approximately 94% for CNN + AG vs 89% for CNN). The needle tip and shaft errors of the CNN + AG method (1.1 mm and 1.8 mm, respectively) were also much smaller than those of the CNN method (2.0 mm and 3.3 mm, respectively). Finally, the dose difference for the HR-CTV D90 using the CNN + AG method was much more accurate than that using CNN (0.4% and 1.7%, respectively)., Conclusions: The attention-added deep-learning model was successfully implemented for automatic needle digitization in HDR brachytherapy, with clinically acceptable geometric and dosimetric accuracy. Compared with conventional deep-learning neural networks, attention-gated deep learning may have superior performance and great clinical potential., (© 2023 The Authors.)

Background: In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI)., Methods: Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (~1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR)., Results: In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI., Conclusions: A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-20-1239). The special issue “Artificial Intelligence for Image-guided Radiation Therapy” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)