Tao Yu

Precise cancer lesion analysis in medical imaging critically depends on the accurate definition of regions of interest (ROIs), which directly influence diagnostic and clinical outcomes. While peritumoral features are known to enhance lesion characterization, efficiently defining meaningful peritumoral ROIs remains a challenge. We propose an adaptive peritumoral area selection approach (APASA) that systematically identifies the most informative ROI surrounding a lesion, enabling the extraction of meaningful radiomic features for improved diagnostic performance. Unlike conventional heuristic or morphology-based methods, APASA leverages the minimum coverage graph algorithm, using the tumor ROI as a reference to construct a graph encompassing both the tumor and its peritumoral microenvironment. The effectiveness of the proposed approach was evaluated within AI-based frameworks for automated lesion differentiation in breast and thyroid cancers. Extensive experiments employing five widely used machine learning models demonstrated that APASA-selected peritumoral features consistently outperformed conventional morphological dilation. Performance improvements reached up to 30.75% in AUC and 29.00% in F1-score compared with the tumor ROI baseline. Moreover, the optimal model was found to vary depending on the ROI type, shape, and cancer type, offering new insights into the interaction between ROI selection and model choice. These results highlight APASA as a principled and efficient strategy for adaptive ROI definition in ultrasound-based cancer lesion analysis, demonstrating effectiveness across two ultrasound datasets, with potential extension to other imaging modalities and clinical settings pending further validation.

Automated breast lesion differentiation in ultrasound (US) imaging has advanced through deep learning (DL) techniques. However, existing 2D approaches predominantly rely on individual static images, overlooking valuable temporal information between frames, which limits their performance. While 3D models can utilize this temporal information, their high computational requirements make them impractical in resource-constrained settings. This study introduces an improved framework that combines spatial and temporal lesion features by incorporating consecutive frames from US videos. Unlike existing 2D models, our framework utilizes a multi-channel input strategy to effectively learn lesion characteristics across frames, avoiding computational burden of 3D models. It offers potential use in resource-limited settings and real-time environments. The framework’s effectiveness has been validated on multicenter data from two different regions. Extensive experiments demonstrate that the proposed multi-channel input technique significantly outperforms single-image approaches. Across five distinct DL backbone models, the framework consistently achieved higher precision, recall, and AUC values, highlighting the positive impact of temporal information on classification accuracy. Specifically, the multi-channel approach yielded improvements of up to 8.6% in AUC, 9.86% in precision, and 23.68% in recall compared to single-image inputs. These results highlight the potential of the proposed multi-channel framework as an effective and practical solution for breast lesion differentiation. By effectively utilizing temporal information without additional computational burden, our proposed approach serves as a computationally efficient alternative to 3D models, advancing DL-based breast US analysis. It shows promising potential for delivering more accurate and accessible diagnostic solutions, making it highly applicable for clinical practice.

This study aims to develop a safe and effective multi-parameter MRI-based molecular subtype prediction model for breast cancer, emphasizing the advantages of this multi-parameter approach over single-parameter models. This study retrospectively collected and organized MRI data from 318 breast cancer patients at Liaoning Provincial Cancer Hospital, including dynamic contrast-enhanced MRI (DCE-MRI, abbreviated as DCE), diffusion weighted MRI (DWI-MRI, abbreviated as DWI), T1-weighted MRI (T1WI-MRI, abbreviated as T1WI), and T2-weighted MRI (T2WI-MRI, abbreviated as T2WI). The dataset includes 57 cases of Luminal A type, 162 cases of Luminal B type, 46 cases of human epidermal growth factor receptor-2 (HER-2) overexpression type, and 53 cases of triple-negative type. Predictive models were established using four single-parameter MRI methods and seven multi-parametric MRI methods, employing quantitative feature extraction. Model performance was evaluated through the area under the curve (AUC) and balanced accuracy (BA). In the single-parameter MRI models, the T2WI-MRI model demonstrated the best predictive performance for four-class classification, with average AUC and BA values of 0.794 and 0.518, respectively. In contrast, the multi-parameter model combining DWI+T2WI exhibited even better performance, with these metrics reaching 0.823 and 0.565, respectively. The multi-parameter feature fusion model for breast cancer molecular subtypes prediction, utilizing DWI+T2WI, exhibited superior BA and AUC values compared to models based solely on single-parameter MRI. It showed enhanced predictive capabilities for Luminal A, Luminal B, HER-2 overexpression, and triple-negative subtypes. Therefore, the multi-parameter MRI-based model offers improved predictive performance over single-parameter models.

Extrachromosomal circular DNA (eccDNA), a chromosome-independent circular DNA, has garnered significant attention due to its widespread distribution and intricate biogenesis in carcinoma. Existing research findings propose that multiple eccDNAs contribute to drug resistance in cancer treatments through complex and interrelated regulatory mechanisms. The unique structure and genetic properties of eccDNA increase tumor heterogeneity. This increased diversity is a result of eccDNA's ability to stimulate oncogene remodeling and participate in anomalous splicing processes through chimeric cyclization and the reintegration of loop DNA back into the linear genome. Such actions promote oncogene amplification and silencing. eccDNA orchestrates protein interactions and modulates protein degradation by acting as a regulatory messenger. Moreover, it plays a pivotal role in modeling the tumor microenvironment and intensifying the stemness characteristics of tumor cells. This review presented detailed information about the biogenesis, distinguishing features, and functions of eccDNA, emphasized the role and mechanisms of eccDNA during cancer treatment, and further proposed the great potential of eccDNA in inspiring novel strategies for precision cancer therapy and facilitating the discovery of prognostic biomarkers for cancer.

Background: Video-assisted thoracoscopic surgery (VATS) has been widely used for the resection of lung lesions. However, it is difficult to palpate or see small lesions, especially ground-glass opacities (GGOs) during VATS. Thoracoscopic ultrasound has definite value in locating pulmonary parenchymal nodules. However, due to the air in the lung parenchyma, its wide application is limited. This study investigated the value of thoracoscopic ultrasound for the localization of GGOs with incomplete lung collapse in VATS. Methods: A retrospective analysis was conducted on patients diagnosed with ground-glass nodules (GGNs) on computed tomography (CT) at Liaoning Province Tumor Hospital from November 2018 to August 2019, who underwent thoracoscopic ultrasound localization and VATS. Screening was conducted for patients who did not achieve complete collapse of the lungs after natural collapse during surgery, the success rate was calculated, and preoperative CT features and thoracoscopic ultrasound features of GGNs were summarized and analyzed. Results: The success rate of GGOs’ localization by thoracoscopic ultrasound in incomplete collapse lung was 56.67%. Of all preoperative CT features, only the distance between the nodule and the pleura was statistically different (P=0.001). The closer the GGO was to the pleura, the easier it was to be detected. Conclusions: Thoracoscopic ultrasound could effectively locate GGO in the condition of incomplete lung collapse and not affect the surgical field, especially when the GGO is close to the pleura, which provides an alternative method for GGO localization in patients who cannot achieve complete lung collapse during VATS.

Deubiquitinating enzymes are a class of proteases that remove ubiquitin tags from proteins, thereby controlling protein stability and function. Tumor inflammation arises from interactions between tumor cells and their microenvironment, which trigger an inflammatory response. The deubiquitinating enzyme USP7 plays a central role in this process. Research suggests that USP7 may modulate various signaling pathways related to inflammatory responses through its deubiquitinating activity, thereby influencing tumor development and progression, including regulating T cell immune activity, improving macrophage anti-tumor activity, and regulating NF-κB signal pathways. Overall, describing the role and mechanism of USP7 in the tumor inflammatory response is of great importance for elucidating the regulatory mechanism of tumor inflammation and developing new therapeutic strategies. This article mainly reviews the structure, function, role, and mechanism of USP7 in the tumor inflammation response.

Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.

Triple negative breast cancer (TNBC) is a type of cancer that lacks receptor expression and has complex molecular mechanisms. Recent evidence shows that the ubiquitin-protease system is closely related to TNBC. In this study, we obtain a key ubiquitination regulatory substrate-ABI2 protein by bioinformatics methods, which is also closely related to the survival and prognosis of TNBC. Further, through a series of experiments, we demonstrated that ABI2 expressed at a low level in TNBC tumors, and it has the ability to control cell cycle and inhibit TNBC cell migration, invasion and proliferation. Molecular mechanism studies proved E3 ligase CBLC could increase the ubiquitination degradation of ABI2 protein. Meanwhile, RNA-seq and IP experiments indicated that ABI2, acting as a crucial factor of tumor suppression, can significantly inhibit PI3K/Akt signaling pathway via the interaction with Rho GTPase RAC1. Finally, based on TNBC drug target ABI2, we screened and found that FDA-approved drug Colistimethate sodium(CS) has significant potential in suppressing the proliferation of TNBC cells and inducing cell apoptosis, making it a promising candidate for impeding the progression of TNBC.