With the widespread application of chemotherapy, immunotherapy, and targeted therapy, cardiotoxicity associated with anti-tumor treatment has gained increasing attention. Drug-induced cardiac injury can significantly impact patients’ quality of life and may even limit the overall efficacy of anti-tumor therapy. The underlying mechanisms include oxidative stress, inflammatory responses, mitochondrial dysfunction, apoptosis, and immune dysregulation. Owing to multitarget effects, low toxicity, and holistic regulatory properties, Chinese traditional medicines have demonstrated considerable potential in cardioprotection. This review summarizes the principal mechanisms of drug-induced myocardial injury related to anti-tumor therapy and highlights recent advances in the prevention and treatment of cardiotoxicity using Chinese medical formulae, such as compound danshen dripping pills, nuanxinkang, qili qiangxin capsules, and shengmai powder, as well as their bioactive constituents. The cardioprotective effects of these agents are discussed in terms of their antioxidative, anti-inflammatory, immunomodulatory, and mitochondrial-protective actions. Furthermore, it highlights certain traditional medicines that exhibit unique advantages in synergistic cardioprotective and anti-tumor therapy. Future efforts should focus on well-designed, systematic clinical studies to facilitate the translational application of integrated Chinese and Western medicine in cardio-oncology.

Ultrasound-guided percutaneous lung needle biopsy (US-PLNB) is a pivotal diagnostic technique for subpleural pulmonary lesions, offering significant advantages such as real-time imaging, absence of ionizing radiation, and high operational flexibility. To standardize its clinical application and enhance both procedural safety and diagnostic accuracy, a multidisciplinary panel of experts was convened by eleven authoritative Chinese academic societies, including Chinese Society of Ultrasound in Medicine. Based on the latest domestic and international evidence-based medicine, this panel developed Chinese expert consensus on ultrasound-guided percutaneous lung needle biopsy (2025 edition). This consensus systematically elaborates on the indications and contraindications, preprocedural assessment and preparation, intraoperative techniques, complication prevention and management, and post-procedural care for US-PLNB. Furthermore, it puts forward 18 specific evidence-based recommendations. The primary aim of this consensus is to provide radiologists and clinicians with standardized operational guidance, promote the standardized application of US-PLNB, and ultimately improve patient care for individuals with pulmonary diseases.

Objective To explore the feasibility and image quality of respiratory-gated total-body ¹⁸F-FDG PET/CT imaging with a 4-minute acquisition in patients with malignant tumors. Methods Sixty-two patients with pathologically confirmed malignant tumors (118 lesions measured) who underwent total-body ¹⁸F-FDG PET/CT examination were consecutively enrolled at the Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, from September 23, 2021 to November 23, 2021, and their clinical and imaging data were retrospectively collected. PET data was acquired over 12 minutes with simultaneous respiratory waveform recorded. The data were reconstructed into four image sets: gated-12 group and gated-4 group (using all counts and the first 4-minute counts, respectively) and ungated-12 group and ungated-6 group (using all counts and the first 6-minute counts, respectively) reconstructed with respiratory gating that retained counts corresponding to the half respiratory window near end-expiration. Image quality assessment and quantitative analysis of lesions were performed and compared among the four image sets, and subgroup analyses were performed according to lesion location, size, and metabolic activity. Results The effective counts of 4 sets of PET images were highest in ungated-12 group, followed by ungated-6 group and gated-12 group, and lowest in gated-4 group (P＜0.001). Regardless of the metabolic level and size of the lesions, compared with the ungated-12 group and the ungated-6 group, the lesions in the gated-12 group showed higher maximum, average, and peak standardized uptake values (SUV_max, SUV_mean, and SUV_peak), tumor liver uptake ratio, tumor blood pool uptake ratio, and smaller metabolic volume (P＜0.05); There was no statistically significant difference in the above indicators between the gate-12 group and the gate-4 group. The background noise of gate-4 group images is slightly higher than that of gate-12 group images (Liver SUV_SD: [0.17＋0.05] vs [0.14＋0.04]); Blood pool SUV_SD: (0.08 ± 0.03) vs (0.06 ± 0.03). There was a statistically significant difference in subjective scores of PET image quality among the four groups (P＜0.001). The subjective scores of image quality in the gated-12 group (4.79 ± 0.41) were not significantly different from those in the non gated-12 group (4.97 ± 0.17) and non gated-6 group (4.79 ± 0.45), but were higher than those in the gated-4 group ([4.17 ± 0.45], P＜0.001)， And the scores have good consistency (Kappa＞0.75). Conclusion In total-body PET/CT imaging for malignant tumors, 4-minute respiratory-gated acquisition can achieve comparable quantitative performance to longer respiratory gating imaging while significantly shortening acquisition time. This approach demonstrates promising potential for clinical application.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Objective To develop a risk assessment scale for immune checkpoint inhibitor (ICI)-associated myocarditis based on multidisciplinary collaboration, and to evaluate its diagnostic performance. Methods Based on multidisciplinary cooperation, integrating clinical experience from oncology and cardiology, literature data, and patient conditions, a risk assessment scale for ICI-associated myocarditis was developed. A total of 101 patients with malignancies who received immunotherapy at Zhongshan Hospital, Fudan University, from October 2020 to October 2024 were included as the validation cohort. Patients were stratified into low-risk (0-1 point), medium-risk (2-4 points), and high-risk (≥5 points) groups based on their scale scores. The association between pretictive risk stratifications and actual assessment results was assessed using the Cox proportional hazards regression model. The predictive value of the scale for ICI-associated myocarditis was evaluated using receiver operating characteristic (ROC) curve. Agreement between the scale scores and actual assessment results was assessed using Cohen’s Kappa coefficient. Results Based on the scale pretictive results, 28(27.7%), 8(7.9%), 65(64.4%) patients were at low risk, medium risk, and high risk for ICI-related myocarditis, respectively; however, 46(45.5%), 8(7.9%), 47(46.5%) were at low risk, medium risk, and high risk actually. Kaplan-Meier survival analysis showed that the cumulative incidence of ICI-related myocarditis in the high-risk group was significantly higher than that in the medium- and low-risk groups (P＜0.05). In the multivariable-adjusted Cox proportional hazards model, the ICI-related myocarditis risk in high-risk group was about 4 times that in the low-risk group. ROC curve analysis demonstrated that the average area under the curve (AUC) for predicting ICI-related myocarditis was 0.81, with an accuracy of 0.74. The Cohen’s Kappa coefficient was 0.55, indicating moderate agreement. In the actual high-risk group, no patient was predicted to be at low risk; in the actual low-risk group, 16 patients were predicted to be at high risk. Conclusions This risk assessment scale for ICI-associated myocarditis shows high predictive performance. It provides oncologists with a simple yet effective multidisciplinary diagnostic reference tool, potentially enhancing early identification of ICI-associated myocarditis.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

INTRODUCTION: Lecanemab has shown promise in treating early Alzheimer's disease (AD), but its safety and efficacy in Chinese populations remain unexplored. This study aimed to evaluate the safety and 6-month clinical outcomes of lecanemab in Chinese patients with mild cognitive impairment (MCI) or mild AD. METHODS: In this single-arm, real-world study, participants with MCI due to AD or mild AD received biweekly intravenous lecanemab (10 mg/kg). The study was conducted at Hainan Branch, Ruijin Hospital Shanghai Jiao Tong University School of Medicine. Patient enrollment and baseline assessments commenced in November 2023. Safety assessments included monitoring for amyloid-related imaging abnormalities (ARIA) and other adverse events. Clinical and biomarker changes from baseline to 6 months were evaluated using cognitive scales (mini-mental state examination [MMSE], montreal cognitive assessment [MoCA], clinical dementia rating-sum of boxes [CDR-SB]), plasma biomarker analysis, and advanced neuroimaging. RESULTS: A total of 64 patients were enrolled in this ongoing real-world study. Safety analysis revealed predominantly mild adverse events, with infusion-related reactions (20.3%, 13/64) being the most common. Of these, 69.2% (9/13) occurred during the initial infusion and 84.6% (11/13) did not recur. ARIA-H (microhemorrhages/superficial siderosis) and ARIA-E (edema/effusion) were observed in 9.4% (6/64) and 3.1% (2/64) of participants, respectively, with only two symptomatic cases (one ARIA-E presenting with headache and one ARIA-H with visual disturbances). After 6 months of treatment, cognitive scores remained stable compared to baseline (MMSE: 22.33 ± 5.58 vs . 21.27 ± 4.30, P = 0.733; MoCA: 16.38 ± 6.67 vs . 15.90 ± 4.78, P = 0.785; CDR-SB: 2.30 ± 1.65 vs . 3.16 ± 1.72, P = 0.357), while significantly increasing plasma amyloid-β 42 (Aβ42) (+21.42%) and Aβ40 (+23.53%) levels compared to baseline. CONCLUSIONS: Lecanemab demonstrated a favorable safety profile in Chinese patients with early AD. Cognitive stability and biomarker changes over 6 months suggest potential efficacy, though high dropout rates and absence of a control group warrant cautious interpretation. These findings provide preliminary real-world evidence for lecanemab's use in China, supporting further investigation in larger controlled studies. REGISTRATION ClinicalTrials.gov , NCT07034222.
Humans ; Alzheimer Disease/drug therapy* ; Male ; Female ; Aged ; Middle Aged ; Cognitive Dysfunction/drug therapy* ; Aged, 80 and over ; Amyloid beta-Peptides/metabolism* ; Biomarkers ; East Asian People

With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
Humans ; Breast Neoplasms/pathology* ; Neoadjuvant Therapy ; Artificial Intelligence ; Female ; Machine Learning ; Deep Learning ; Chemotherapy, Adjuvant ; Treatment Outcome