With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.
ObjectiveTo evaluate the application value of three-dimensional (3D) reconstruction in preoperative surgical diagnosis of new classification criteria for lung adenocarcinoma, which is helpful to develop a deep learning model of artificial intelligence in the auxiliary diagnosis and treatment of lung cancer.MethodsThe clinical data of 173 patients with ground-glass lung nodules with a diameter of ≤2 cm, who were admitted from October 2018 to June 2020 in our hospital were retrospectively analyzed. Among them, 55 were males and 118 were females with a median age of 61 (28-82) years. Pulmonary nodules in different parts of the same patient were treated as independent events, and a total of 181 subjects were included. According to the new classification criteria of pathological types, they were divided into pre-invasive lesions (atypical adenomatous hyperplasia and and adenocarcinoma in situ), minimally invasive adenocarcinoma and invasive adenocarcinoma. The relationship between 3D reconstruction parameters and different pathological subtypes of lung adenocarcinoma, and their diagnostic values were analyzed by multiplanar reconstruction and volume reconstruction techniques.ResultsIn different pathological types of lung adenocarcinoma, the diameter of lung nodules (P<0.001), average CT value (P<0.001), consolidation/tumor ratio (CTR, P<0.001), type of nodules (P<0.001), nodular morphology (P<0.001), pleural indenlation sign (P<0.001), air bronchogram sign (P=0.010), vascular access inside the nodule (P=0.005), TNM staging (P<0.001) were significantly different, while nodule growth sites were not (P=0.054). At the same time, it was also found that with the increased invasiveness of different pathological subtypes of lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. Meanwhile, nodule diameter and the average CT value or CTR were independent risk factors for malignant degree of lung adenocarcinoma.ConclusionImaging signs of lung adenocarcinoma in 3D reconstruction, including nodule diameter, the average CT value, CTR, shape, type, vascular access conditions, air bronchogram sign, pleural indenlation sign, play an important role in the diagnosis of lung adenocarcinoma subtype and can provide guidance for personalized therapy to patients in clinics.
Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.
Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disease caused by the mutations in the NF1 gene, with an incidence of approximately 1/3 000. Affecting multiple organs and systems throughout the body, NF1 caused a wide variety of clinical symptoms. A comprehensive multidisciplinary diagnostic and treatment model is needed to meet the diverse needs of NF1 patients and improve their quality of life. In recent years, the emergence of targeted therapies has further benefited NF1 patients, and the number of clinical consultations has increased dramatically. However, due to the rarity of the disease itself and insufficient attention previously, the standardized, systematic, and precise diagnosis and treatment model of NF1 still needs to be further improved. In this paper, we reviewed the current status of comprehensive diagnosis and treatment of NF1 in China, combine with our long-term experiences in diagnosis and treatment of this disease. Meanwhile, we propose future directions and several suggestions for the comprehensive diagnosis and treatment model for Chinese NF1 patients.
Gastric cancer remains one of the most prevalent and fatal malignancies in China. Peritoneal metastasis represents a frequent mode of dissemination or recurrence in patients with advanced disease and confers an extremely poor prognosis. In recent years, considerable progress has been made in imaging techniques, with modalities including CT, ultrasound, MRI and PET-CT being implemented to evaluate peritoneal metastasis. However, adequate detection remains challenging, particularly for occult peritoneal metastasis. With the advent of precision medicine, radiomics and artificial intelligence have undergone rapid development and show considerable promise for the early prediction of peritoneal metastasis in gastric cancer, providing a new means of diagnosis and treatment for patients with peritoneal metastasis.
Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.
ObjectiveTo systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy (RT), explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. MethodsA comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes and organs at risk, intelligent treatment planning, and prediction of RT-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. ResultsAI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporated multimodal data enabled more accurate, personalized risk assessment, supporting safer and more effective individualized RT. ConclusionsRT is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of RT, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in RT.
ObjectiveTo review and evaluate the research progress of the robot-assisted joint arthroplasty.MethodsThe domestic and foreign related research literature on robot-assisted joint arthroplasty was extensively consulted. The advantages, disadvantages, effectiveness, and future prospects were mainly reviewed and summarized.ResultsThe widely recognized advantages of robot-assisted joint arthroplasty are digital and intelligent preoperative planning, accurate intraoperative prosthesis implantation, and quantitative soft tissue balance, as well as good postoperative imaging prosthesis position and alignment. However, the advantages of effectiveness are still controversial. The main disadvantages of robot-assisted joint arthroplasty are the high price of the robot system, the prolonged operation time, and the increased radioactive damage of the imaging-dependent system.ConclusionCompared to traditional arthroplasty, robot-assisted joint arthroplasty can improve the accuracy of the prosthesis position and assist in the quantitative assessment of soft tissue tension, and the repeatability rate is high. In the future, further research is needed to evaluate the clinical function and survival rate of the prosthesis, as well as to optimize the robot system.
ObjectiveTo better understand artificial intelligence (AI) and its application in management of liver cancer.MethodThe relevant literatures about AI in the diagnosis and treatment of liver cancer in recent years were reviewed.ResultsIn terms of diagnosis, the deep learning could precisely and quickly complete the imaging localization and segmentation of the liver, which was helpful for the diagnosis, while radiomics had a high value in assisting the diagnosis of liver cancer, predicting the postoperative recurrence and long-term survival of patients with liver cancer. In regard of treatment, although it was still difficult for AI to intervene in liver surgery, it had significant advantages in formulating individualized operation scheme for patients with liver cancer, which enabled precise hepatectomy and was helpful for prediction of intraoperative bleeding. AI fusion imaging could provide assistance in operation plan making and realize the precise placement of ablation needle. AI was able to predict the tumor response or even tumor progression after interventional therapy and radiotherapy. Pathological analysis was also facilitated by AI and was able to identify some details and feature textures that were difficult to manually distinguish. For transplantation, guidance of AI on the allocation of donor livers based on hazards models helped make better use of limited organ resources. AI could be applied in prognosis prediction in almost all treatment modalities.ConclusionsAI provides more efficient and precise diagnosis, treatment support and prognosis than conventional medical process in liver cancer, generally by constructing a fully functional model based on a series of data mining methods combined with statistical analysis.
The incidence of lung cancer has increased significantly during the past decades. Pathology is the gold standard for diagnosis and the corresponding treatment measures selection of lung cancer. In recent years, with the development of artificial intelligence and digital pathology, the researches of pathological image analysis have achieved remarkable progresses in lung cancer. In this review, we will introduce the research progress on artificial intelligence in pathological classification, mutation genes and prognosis of lung cancer. Artificial intelligence is expected to further accelerate the pace of precision pathology.