Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. MethodsThe patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.
Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.
ObjectiveTo explore the clinical, imaging, and pathological features of patients with synchronous double primary hepatocellular carcinoma and intrahepatic cholangiocarcinoma (sdpHCC-ICC), to enhance our understanding of the disease and reduce the rate of misdiagnosis and missed diagnosis.MethodsThe clinical, imaging, and pathological data of patients who were histologically confirmed as sdpHCC-ICC in West China Hospital of Sichuan University between January 1st 2014 and December 31st 2018 were studied retrospectively.ResultsA total of 11 patients with sdpHCC-ICC were screened for the study, of which 10 were male and 1 was female. The median age of patients was 55.6 years (ranged from 47 to 73 years). Eight patients were chronically infected with hepatitis B virus. Both increased alpha-fetoprotein and carbohydrate antigen 19-9 were observed in 8 patients. Contrast enhanced CT was performed in 8 cases, color doppler ultrasound in 4 cases, enhanced MRI in 3 cases, and contrast-enhanced ultrasound in 1 case. Among them, one solitary lesion was found in 2 patients, and two or more lesions were observed in 9 patients. Most of the patients had typical imaging performance of hepatocellular carcinoma (HCC): 8 patients showed strong enhancement of HCC during the hepatic arterial phase and progressive hyper-attenuation on venous and delayed phases, 1 patient showed peripheral rim enhancement in the arterial phase of intrahepatic cholangiocarcinoma (ICC) in another lesion could be observed at the same time. None of the 11 patients with sdpHCC-ICC was diagnosed accurately before operation. All patients underwent surgical treatment. HCC lesions were distributed in all parts of the liver, while ICC lesions were located in the right lobe of the liver in 10 cases. The median diameter of HCC and ICC was 3.5 cm and 2.1 cm, respectively. All of them were confirmed by hematoxylin-eosin staining and immunohistochemistry.ConclusionsThe clinical characteristics of sdpHCC-ICC are usually atypical. It is difficult to make an accurate preoperative diagnosis. Tumor markers may be valuable to the diagnosis of sdpHCC-ICC. The definite diagnosis of sdpHCC-ICC depends on pathological examination.
ObjectiveTo explore the value and role of post-processing techniques such as 3D reconstruction in the online education mode in neurosurgery undergraduate clinical probation teaching.MethodsA retrospective analysis method was used to collect 120 clinical 5-year medical students who were trained in neurosurgery at West China Hospital of Sichuan University from January 2019 to May 2020, including 40 students receiving traditional imaging materials offline (control group 1), 40 students being taught on image post-processing technology offline (control group 2), and 40 students being taught on-line image post-processing technology during the novel coronavirus epidemic (observational group). The students’ scores of departmental rotation examination and feedback survey results on teaching satisfaction were collected, and multiple comparison was conducted between the observational group and the two control groups respectively.ResultIn the control group 1, the control group 2, and the observational group, the theoretical test scores were 36.80±3.22, 38.17±2.61, and 38.97±2.79, respectively; the case analysis scores were 37.05±2.01, 38.40±2.62, and 39.25±2.88, respectively; the total scores were 73.85±5.06, 76.57±4.29, and 78.10±4.53, respectively; the scores of interest in teaching were 84.47±3.71, 86.05±2.87, and 86.82±2.60, respectively; the scores of mastery of knowledge were 82.85±4.39, 84.90±2.72, and 85.78±2.36, respectively; and the scores of overall satisfaction with teaching were 84.17±3.45, 85.97±2.64, and 86.37±2.59, respectively. The differences among the three groups were all statistically significant (P<0.05). The observational group differed significantly from the control group 1 in all the above scores (P<0.05), while did not differed from the control group 2 in any of the above scores (P>0.05).ConclusionsIn neurosurgery internship activities, the online application of image post-processing techniques such as 3D reconstruction will help students establish 3D spatial concepts, better understand the brain anatomy, and improve students’ academic performance and acceptance.
Deep brain stimulation (DBS) surgery is an important treatment for patients with Parkinson's disease in the middle and late stages. The accuracy of the implantation of electrode at the location of the nuclei directly determines the therapeutic effect of the operation. At present, there is no single imaging method that can obtain images with electrodes, nuclei and their positional relationship. In addition, the subthalamic nucleus is small in size and the boundary is not obvious, so it cannot be directly segmented. In this paper, a complete end-to-end DBS effect evaluation pipeline was constructed using magnetic resonance (MR) data of T1, T2 and SWI weighted by DBS surgery. Firstly, the images of preoperative and postoperative patients are registered and normalized to the same coordinate space. Secondly, the patient map is obtained by non-rigid registration of brain map and preoperative data, as well as the preoperative nuclear cluster prediction position. Then, a three-dimensional (3D) image of the positional relationship between the electrode and the nucleus is obtained by using the electrode path in the postoperative image and the result of the nuclear segmentation. The 3D image is helpful for the evaluation of the postoperative effect of DBS and provides effective information for postoperative program control. After analysis, the algorithm can achieve a good registration between the patient's DBS surgical image and the brain map. The error between the algorithm and the expert evaluation of the physical coordinates of the center of the thalamus is (1.590 ± 1.063) mm. The problem of postoperative evaluation of the placement of DBS surgical electrodes is solved.
