Artificial intelligence(AI)has revolutionized the field of medical imaging,particularly in diagnosis and treatment of renal tumors.The research progresses and future development trends of AI applicated in medical imaging of renal tumors were reviewed in this article.

Colorectal cancer is one of the most common malignant tumors of the digestive system in clinical practice.The early detection of colorectal cancer based on artificial intelligence and its further assistance in clinical diagnosis and treatment hold significant clinical importance for achieving long-term benefits for patients.The development and validation of artificial intelligence software rely on high-quality,large-volume,and annotated colorectal cancer imaging datasets.This paper aims to provide a reference for constructing a high-quality colorectal cancer CT database,taking the construction of the database as an example.It discusses the complete process of database establishment,including database description,lesion annotation and storage,database quality evaluation and maintenance.The purpose is to ensure the high quality and exploitability of the source materials in the database,promote the sustainable and healthy development of the medical imaging artificial intelligence industry ecosystem,and accelerate the research,development,and application of industries related to artificial intelligence in colorectal cancer CT imaging.

As the key channel of hepatic blood circulation, the portal vein plays a key role in the planning and implementation of minimally invasive liver surgery based on its branch morphology, location, and hemodynamic information. Intelligent imaging technology not only provides more reliable anatomical basis for precision liver resection, but also opens up new possibilities for per-sonalized planning and precise implementation of minimally invasive liver resection. The authors will review the application of portal vein imaging in minimally invasive surgery.

Colorectal cancer is one of the most common malignant tumors of the digestive system in clinical practice.The early detection of colorectal cancer based on artificial intelligence and its further assistance in clinical diagnosis and treatment hold significant clinical importance for achieving long-term benefits for patients.The development and validation of artificial intelligence software rely on high-quality,large-volume,and annotated colorectal cancer imaging datasets.This paper aims to provide a reference for constructing a high-quality colorectal cancer CT database,taking the construction of the database as an example.It discusses the complete process of database establishment,including database description,lesion annotation and storage,database quality evaluation and maintenance.The purpose is to ensure the high quality and exploitability of the source materials in the database,promote the sustainable and healthy development of the medical imaging artificial intelligence industry ecosystem,and accelerate the research,development,and application of industries related to artificial intelligence in colorectal cancer CT imaging.

As the key channel of hepatic blood circulation, the portal vein plays a key role in the planning and implementation of minimally invasive liver surgery based on its branch morphology, location, and hemodynamic information. Intelligent imaging technology not only provides more reliable anatomical basis for precision liver resection, but also opens up new possibilities for per-sonalized planning and precise implementation of minimally invasive liver resection. The authors will review the application of portal vein imaging in minimally invasive surgery.

Artificial intelligence(AI)has revolutionized the field of medical imaging,particularly in diagnosis and treatment of renal tumors.The research progresses and future development trends of AI applicated in medical imaging of renal tumors were reviewed in this article.

Objective:To investigate the application status of emergency radiology in China, and to provide data support for the standardized development, scientific management and big data research of emergency radiology.Methods:From August 12th to October 19th, 2022, a questionnaire survey was conducted through WeChat"Questionnaire Star"to send targeted questionnaires to investigate the relevant data of the current status of emergency radiology in China, mainly including digital radiography (DR) and computed tomography (CT). This study was initiated by the Chinese Emergency Radiology Database Collaboration Group, and comprehensively investigated emergency imaging personnel, equipment, workload, critical value reporting process, and artificial intelligence (AI) application status.Results:There were 123 hospitals in the study. The survey showed that emergency DR/CT reports were mainly completed by residents and above (69.1%). There were 21 DR brands, 10 CT brands and 8 MR brands used for emergency imaging examinations. The median number of DR examinations in tertiary hospitals and secondary hospitals investigated from January to June 2022 was 4 642 and 2 015 cases respectively, and the median number of CT examinations was 16 512 and 3 762 cases respectively. The average single-shift workload of DR in the emergency radiology department during the day and night shift in tertiary hospitals was mainly ≤20 copies and 21-50 copies, and the average single-shift workload of CT in the emergency radiology department during the day and night shift was mainly 21-50 copies and 51-100 copies, while the average single-shift workload of DR/CT in the emergency radiology department during the day/night shift in secondary hospitals was mainly ≤20 copies. In terms of critical value reporting process, 74.8% of emergency imaging doctors and 84.6% of emergency imaging technicians took the way of phone/text message to notify the clinical doctor or the patients′ family. The overall deployment rate of AI in emergency imaging was about 60.2%. 75% of the respondents believed that in the future, AI can improve emergency radiology work from aspects such as emergency screening, aided diagnosis and process optimization.Conclusions:The emergency medical imaging mainly based on DR and CT has the current situations such as generally low seniority of doctors, diverse brands of imaging equipments, large volume of examinations and intense workload per doctor, especially in tertiary hospitals, and dependence on traditional means for critical value reporting. At present, AI is emerging in the field of emergency imaging, and there is still a long way to go to play the huge potential of AI in the intelligent whole process of emergency imaging in the future.

Objective:To establish a clinical diagnostic scoring model for preoperative predicting hepatocellular carcinoma (HCC) microvascular invasion (MVI) based on gadolinium-ethoxybenzyl-diethylenetriamine pentacetic acid (Gd-EOB-DTPA) enhanced MRI, and verify its effectiveness.Methods:From January 2014 to December 2020, a total of 251 cases with pathologically confirmed HCC from Tianjin First Central Hospital and Jilin University First Hospital were retrospectively collected to serve as the training set, while 57 HCC patients from Tianjin Medical University Cancer Hospital were recruited as an independent external validation set. The HCC patients were divided into MVI positive and MVI negative groups according to the pathological results. The tumor maximum diameters and apparent diffusion coefficient (ADC) values were measured. On the Gd-EOB-DTPA MRI images, tumor morphology, peritumoral enhancement, peritumoral low intensity (PTLI), capsule, intratumoral artery, intratumoral fat, intratumoral hemorrhage, and intratumoral necrosis were observed. Univariate analysis was performed using the χ 2 test or the independent sample t-test. The independent risk factors associated with MVI were obtained in the training set using a multivariate logistic analysis. Points were assigned to each factor according to the weight value to establish a preoperative score model for predicting MVI. The receiver operating characteristic (ROC) curve was used to determine the score threshold and to verify the efficacy of this scoring model in predicting MVI in the independent external validation set. Results:The training set obtained 98 patients in the MVI positive group and 153 patients in the MVI negative group, while the external validation set obtained 16 patients in the MVI positive group and 41 patients in the MVI negative group. According to logistic analysis, tumor maximum diameter>3.66 cm (OR 3.654, 95%CI 1.902-7.018), hepatobiliary PTLI (OR 9.235, 95%CI 4.833-16.896) and incomplete capsule (OR 6.266, 95%CI 1.993-9.345) were independent risk factors for MVI in HCC, which were assigned scores of 3, 4 and 2, respectively. The total score ranged from 0 to 9. In the external validation set, ROC curve analysis showed that the area under the curve of the scoring model was 0.918 (95%CI 0.815-0.974, P=0.001). When the score>4 was used as the threshold, the accuracy, sensitivity, and specificity of the model in predicting MVI were 84.2%, 81.3%, and 85.4%, respectively. Conclusions:A scoring model based on Gd-EOB-DTPA-enhanced MRI provided a convenient and reliable way to predict MVI preoperatively.

Objective:To explore the current status of the artificial intelligence (AI) developments in medical imaging in China, and to provide data for the development of AI.Methods:In May 2022, the Radiology Branch of the Chinese Medical Association and the China Medical Imaging AI Industry-University-Research Innovation Alliance jointly launched a nationwide survey on the application status and development needs of medical imaging AI in China in the form of a questionnaire. This survey was carried out for different groups of people, focusing on the clinical applications of medical imaging AI, enterprise development, and educational needs in colleges and universities, with the descriptive statistical analysis performed.Results:China′s medical imaging AI has made great progress in clinical applications, in enterprise developments, as well as in the education and teaching areas. In terms of clinical application, 90.8% (5 765/6 347) of the survey respondents had a preliminary understanding of AI. There were 62.1% (3 798/6 119) doctors confirmed the applications medical imaging AI products in their departments. AI products were applied in the whole process of medical imaging examination, especially in assistance of the diagnosis. The application of pulmonary nodules screening accounted for 89.5% (3 401/3 798) of all medical imaging AIs. The main factors restricting the rapid development of medical imaging AI included lack of experts [47.3% (3 002/6 347)], poor data quality [45.7% (2 898/6 347)] and imperfect function of the products [40.4% (2 566/6 347)]; in terms of enterprises, there were 65.4% enterprises with a scale of less than 100 employees (17/26), and 34.6% with a scale of more than 100 employees (9/26). The main group of the customers were the hospitals above the second level, accounting for about 92.3% (24/26); in terms of education, the number and quality of AI courses, practical operations and lectures currently carried out by schools vary between different levels. The AI courses for graduated students accounted for about 22.5% (86/381), which were the largest in number; while the proportion of AI courses for junior college students, undergraduates and regular trainees were less than 15%. More than 60% of the students thought it necessary for schools to establish AI courses. Among all the students, the master′s and doctoral candidates had the greatest demand for additional AI courses [84.8% (323/381)].Conclusions:The development and popularization of medical imaging AI in China continues to prosper, with opportunities and challenges coexisting. It is necessary to adhere to the orientation of clinical needs, and to realize the coordinated development of clinical application, enterprise development, as well as education and teaching.