Postpartum chylothorax is an infrequent complication of delivery that is sometimes overlooked. We presented 2 cases of chylothorax in primiparous women who developed chest tightness and breath shortness after vaginal birth, probably due to increased pressure in the thoracic ducts during labor. Lymphography with iodine oil revealed leakage at the T4 level of the thoracic duct in 1 patient but not in the other. Only trace amounts of iodized oil were deposited in the thoracic cavity. There was a significant decrease in postoperative drainage. However, the treatment did not yield the anticipated curative effect in either case. Eight incidences of postpartum chylothorax were identified in the reviewed literature. Patients with refractory chylothorax may benefit greatly from lymphography since it can detect structural changes and determine whether there is a leaking in the thoracic duct. Lymphography-guided therapy for chylothorax with a verified leak has the potential to be both effective and safe. Lymphangiography can serve as a useful tool in selecting the optimal surgical strategy.

目的:探讨在机器深度学习中迁移学习在图像软件对实验动物解剖结构的识别、提取和自动分割中的作用,以及数据增强算法对迁移学习能力的影响. 方法:在HyVision Ablation Planning V1.0 图像软件的平台上,以Efficient Net b1神经网络作为深度学习的骨干网络.利用 51 套VX2 兔肝癌模型的计算机断层扫描(computed tomography,CT)图像,以数据增强的方式进行迁移学习训练.将图像软件已经具备的人体腹部CT图像上器官的识别、提取与自动分割功能在动物模型上进行重现.比较不同的学习模型和算法模型的Dice系数、归一化表面Dice(normalized surface Dice,NSD)、三维重建的图像质量及与医师标注的动物模型训练集的差异. 结果:从有数据增强无迁移学习型的模型到有数据增强有迁移学习型的模型,VX2 兔CT图像的器官自动分割Dice系数从 0.525 提升到 0.676,提高了 28.76%,NSD从 0.448 提升到 0.616,提高了 37.50%.从无数据增强有迁移学习型的模型到有数据增强有迁移学习型的模型,VX2 兔CT图像的器官自动分割Dice系数从 0.502 提升到 0.676,提高了 34.66%,NSD从 0.459 提升到 0.616,提高了 34.20%.表明在机器深度学习过程中迁移学习与数据增强对于研究新的解剖对象同等重要. 结论:在机器深度学习过程中,迁移学习的功能可以借助数据增强算法,获得更好的图像识别、提取与自动分割的结果.

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

腹腔积液在腹部外科术后较为常见，如合并感染、胆漏、胰漏或吻合口漏，常常需要积极引流或手术干预[1]。影像引导下经皮引流，因操作简便、安全，被推荐为首选治疗方式[2,3]。而术后腹腔局部炎症渗出，结构界限不清，腹部切口干扰，患者体位受限以及积液被周围脏器包绕等因素，常常没有直接经皮引流的路径[4]。对于靠近肝脏的腹腔积液，1985年Mueller等[5]首次报道了"经皮经肝置管"进行术后腹腔积液的引流，其后，陆续也有相关的文献报道，但临床上对经皮经肝脏穿刺、留置大口径的导管引流，有很多顾虑。在此，我们对2例腹部手术后腹腔积液，影像引导下经皮经肝置管引流成功的病例进行报道并文献回顾。