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Objective: BRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methods: In this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. Results: In the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647–0.776) vs 0.768 (95% CI, 0.704–0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597–0.808) vs 0.723 (95% CI, 0.625–0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748–0.861) in the training dataset and 0.811 (95% CI, 0.724–0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong’s test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. Conclusion: This machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.

Summary: Background: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. Methods: This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. Findings: The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865–0.968) in the internal validation cohort, and 0.900 (95% CI 0.819–0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719–0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704–0.878], p = 0.013) in the external test cohort. Interpretation: The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. Funding: Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).

Background and AimsMicrovascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC.MethodsA total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI.ResultsCompared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1–7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7–21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23–8.91], p = 0.011) in the test group.ConclusionsThe proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis.

BackgroundThis study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features.MethodsA retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram.ResultsIn univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81–0.92) and 0.84 (95% CI, 0.73–0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram.ConclusionsThis study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.

Breast cancer is frequently diagnosed using ultrasound. Here, the authors show that, in addition to ultrasound, shear wave elastography can be used to diagnose breast cancer and, in conjunction with deep learning and radiomics, can predict whether the disease has spread to axillary lymph nodes.

Abstract Background This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors. Methods Clinically responding (n = 20; MCF-7) and nonresponding (n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters (slope, intercept, S1, S2, S3 and S4) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed. Results Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders (P 0.05). Fold changes of slope were significantly increased in responders two days after adriamycin treatment (P = 0.002), but not in nonresponders (P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9–100.0%) and nonresponders (95% CI, 88.4–100%). Conclusions Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders.

Abstract The Legionella pneumophila effector MavC is a transglutaminase that carries out atypical ubiquitination of the host ubiquitin (Ub)‐conjugation enzyme UBE2N by catalyzing the formation of an isopeptide bond between Gln40Ub and Lys92UBE2N, which leads to inhibition of signaling in the NF‐κB pathway. In the absence of UBE2N, MavC deamidates Ub at Gln40 or catalyzes self‐ubiquitination. However, the mechanisms underlying these enzymatic activities of MavC are poorly understood at the molecular level. This study reports the structure of the MavC–UBE2N–Ub ternary complex representing a snapshot of MavC‐catalyzed crosslinking of UBE2N and Ub, which reveals the way by which UBE2N–Ub binds to the Insertion and Tail domains of MavC. Based on the structural and experimental data, the catalytic mechanism for the deamidase and transglutaminase activities of MavC is proposed. Finally, by comparing the structures of MavC and MvcA, the homologous protein that reverses MavC‐induced UBE2N ubiquitination, several essential regions and two key residues (Trp255MavC and Phe268MvcA) responsible for their respective enzymatic activities are identified. The results provide insights into the mechanisms for substrate recognition and ubiquitination mediated by MavC as well as explanations for the opposite activity of MavC and MvcA in terms of regulation of UBE2N ubiquitination.

Proteasomes are highly abundant and conserved protease complexes that eliminate unwanted proteins in the cells. As a single-chain ATP-independent nuclear proteasome activator, proteasome activator 200 (PA200) associates with 20S core particle to form proteasome complex that catalyzes polyubiquitin-independent degradation of acetylated histones, thus playing a pivotal role in DNA repair and spermatogenesis. Here, we present cryo-electron microscopy (cryo-EM) structures of the human PA200-20S complex and PA200 at 2.72 Å and 3.75 Å, respectively. PA200 exhibits a dome-like architecture that caps 20S and uses its C-terminal YYA (Tyr-Tyr-Ala) to induce the α-ring rearrangements and partial opening of the 20S gate. Our structural data also indicate that PA200 has two openings formed by numerous positively charged residues that respectively bind (5,6)-bisdiphosphoinositol tetrakisphosphate (5,6[PP]2-InsP4) and inositol hexakisphosphate (InsP6) and are likely to be the gates that lead unfolded proteins through PA200 and into the 20S. Besides, our structural analysis of PA200 found that the bromodomain (BRD)-like (BRDL) domain of PA200 shows considerable sequence variation in comparison to other human BRDs, as it contains only 82 residues because of a short ZA loop, and cannot be classified into any of the eight typical human BRD families. Taken together, the results obtained from this study provide important insights into human PA200-induced 20S gate opening for substrate degradation and the opportunities to explore the mechanism for its recognition of H4 histone in acetylation-mediated proteasomal degradation.

The Earthsea Cycle by Ursula. K. Le Guin is a fantasy work in Western literature that shines with ostentatiously idiosyncratic sparks of Taoist philosophies. Resorting to Taoism (also translated as Daoism) and its representative work Tao Te Ching, this article aims at exploring the Earthsea magic, a ubiquitous motif in fantasy, with Taoist thoughts and theories including the law of relativity, harmonious dialectics, and equilibrium. This article reconstructs the magical Earthsea world within a Taoist framework and reveals the Taoist connotations of magic. Finally, this article concludes that, radically distinct from its traditional image, magic in Earthsea serves to heal the physical, mental, and spiritual wound of separation; set up harmony of the opposites in binaries; and preserve the delicate equilibrium insusceptible to the ravages of time. Magic in The Earthsea Cycle works miracles in a Taoist manner.

Abstract Anti‐angiogenic therapies targeting inhibition of vascular endothelial growth factor (VEGF) pathway show clinical benefit in hypervascular hepatocellular carcinoma (HCC) tumors. However, HCC expresses massive pro‐angiogenic factors in the tumor microenvironment (TME) in response to anti‐angiogenic therapy, recruiting tumor‐associated macrophages (TAMs), leading to revascularization and tumor progression. To regulate cell types in TME and promote the therapeutic efficiency of anti‐angiogenic therapy, a supramolecular hydrogel drug delivery system (PLDX‐PMI) co‐assembled by anti‐angiogenic nanomedicines (PCN‐Len nanoparticles (NPs)) and oxidized dextran (DX), and loaded with TAMs‐reprogramming polyTLR7/8a nanoregulators (p(Man‐IMDQ) NRs) is developed for orthotopic liver cancer therapy. PCN‐Len NPs target tyrosine kinases of vascular endothelial cells and blocked VEGFR signaling pathway. p(Man‐IMDQ) NRs repolarize pro‐angiogenic M2‐type TAMs into anti‐angiogenic M1‐type TAMs via mannose‐binding receptors, reducing the secretion of VEGF, which further compromised the migration and proliferation of vascular endothelial cells. On highly malignant orthotopic liver cancer Hepa1‐6 model, it is found that a single administration of the hydrogel formulation significantly decreases tumor microvessel density, promotes tumor vascular network maturation, and reduces M2‐subtype TAMs, thereby effectively inhibiting tumor progression. Collectively, findings in this work highlight the great significance of TAMs reprogramming in enhancing anti‐angiogenesis treatment for orthotopic HCC, and provides an advanced hydrogel delivery system‐based synergistic approach for tumor therapy.