Objective To explore the value of chemosensitivity assay in vitro on breast cancer. Methods In vitro chemosensitivity of 6 species of chemotherapeutic agents applied to 38 cases of breast cancer patients were detected by tissue culture-end point staining-computer image analysis (TECIA). Results The sensitivity to chemotherapeutic agents commonly used in the breast cancer level from high to low was as follow: Doxorubicin (ADM), Paclitaxel (TAX), Vinorelbine (NVB), Cyclophosphamide (CTX), Cisplatin (DDP) and Fluorouracil (FU). Conclusion Drugs sensitivity experiment of cancer in vitro by TECIA has an important value to instruct clinical medication and individual chemotherapy for breast cancer.
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.
ObjectiveTo observe the asynchrony patterns between left and right lungs in smokers and non-smokers,to assess the role of vibration response imaging(VRI) in the early detection and evaluation of smoking-related lung abnormalities.
MethodsData were collected as follows:(1)past history and smoking history were collected;(2)exhaled CO test to confirm smoking status was performed;(3)VRI test was performed and the curve of Breath Energy Unit(BEU)was drawn,which is an energy versus time graph of the breath energy.The asynchrony between left and right lungs was derived from this graph;(4)pulmonary function test was performed.In the end,26 villagers with normal spirometry findings were included in the study.The subjects were divided into an ever-smoking group and a never-smoking group.
ResultsThe BEU lung asynchrony was 2.0(3.0) frame in the never-smoking group,and 2.0(3.0) frame too in the ever-smoking group.Rank sum test showed that there was no significant difference(Z=-0.29,P=0.77) between the never-smokers and the ever-smokers in the lung asynchrony.Rank correlation analysis suggested that in the ever-smoking group,smoking index and BEU asynchrony had significant correlation(r=0.61,P=0.03).In the never-smoking group,the coefficient of passive smoking index and lung asynchrony was 0.52(P=0.07).The P value of the coefficient between passive smoking index and lung asynchrony was nearly 0.05,scatter between them could be seen a presence of a certain trend.
ConclusionThe lung asynchrony in VRI has dose-effect relationship with ever-smokers' smoking level(smoking index).Thus,the lung abnormalities in VRI caused by the exposure to passive smoking is maybe the same as the abnormalities caused by direct smoking.
Retinopathy of prematurity (ROP) is a major cause of vision loss and blindness among premature infants. Timely screening, diagnosis, and intervention can effectively prevent the deterioration of ROP. However, there are several challenges in ROP diagnosis globally, including high subjectivity, low screening efficiency, regional disparities in screening coverage, and severe shortage of pediatric ophthalmologists. The application of artificial intelligence (AI) as an assistive tool for diagnosis or an automated method for ROP diagnosis can improve the efficiency and objectivity of ROP diagnosis, expand screening coverage, and enable automated screening and quantified diagnostic results. In the global environment that emphasizes the development and application of medical imaging AI, developing more accurate diagnostic networks, exploring more effective AI-assisted diagnosis methods, and enhancing the interpretability of AI-assisted diagnosis, can accelerate the improvement of AI policies of ROP and the implementation of AI products, promoting the development of ROP diagnosis and treatment.
Quantitatively analyzing hematoxylin & eosin (H&E) histopathology images is an emerging field attracting increasing attentions in recent years. This paper reviews the application of computer-aided image analysis in breast cancer prognosis. The traditional prognosis based on H&E histopathology image for breast cancer is firstly sketched, followed by a detailed description of the workflow of computer-aided prognosis including image acquisition, image preprocessing, regions of interest detection and object segmentation, feature extraction, and computer-aided prognosis. In the end, major technical challenges and future directions in this field are summarized.
Forty two eyes of 37 coses of age-related macular degeneration with subretinal neovascularization were followed up for more than 5 yeors.Vision, fundus change and fluorescein angiograms were with electrical image analysis system.
（Chin J Ocul Fundus Dis,1994,10:1-3）
Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.
ObjectivesTo develop a fundus photography (FP) image lesion recognition model based on the EfficientNet lightweight convolutional neural network architecture, and to preliminary evaluate its recognition performance. MethodsA diagnostic test. The data was collected in the Department of Ophthalmology at Sichuan Provincial People's Hospital from June 2023 to June 2025. A lightweight 16-category lesion recognition model was constructed based on deep learning and 610 072 FP images. The FP images were sourced from Sichuan Provincial People's Hospital as well as the APTOS, Diabetic Retinopathy_2015, Diabetic Retinopathy_2019, and Retinal Disease datasets. Model performance was evaluated as follows: first, testing was performed on four independent external validation sets using metrics such as accuracy, F1 score (the harmonic mean of precision and recall), and the area under the receiver operating characteristic curve (AUC) to measure the model's generalizability and accuracy. Second, the classification results of the model were compared with those of junior and mid-level ophthalmologists (two each) using the overlapping confidence interval (CI) comparison method to assess the clinical experience level corresponding to the model's medical proficiency. ResultsThe model achieved an accuracy of 96.78% (59 039/61 003), an F1 score of 82.51% (50 334/61 003), and an AUC of 99.93% (60 960/61 003) on the validation set. On the four external validation sets, it achieved an average accuracy of 87.77% (57 358/65 350), an average precision of 87.06% (56 894/65 350), and an average Kappa value of 82.28%. The average accuracy of FP image lesion identification for junior and mid-level ophthalmologists was 79.00% (79/100) (95%CI 67.71-90.29) and 87.00% (87/100) (95%CI 77.68-96.32), respectively. ConclusionsA 16-category FP image lesion recognition model is successfully constructed based on the EfficientNet lightweight convolutional neural network architecture. Its clinical performance preliminarily reaches the level of mid-level ophthalmologists.
To solve the problem that the method based on tumor morphology or overall average parameters of tumor cannot conduct the early evaluation of tumor treatment response, we proposed a voxel-wise method. The voxel-wise method uses the method combining rigid and elastic registration algorithm to align the tumor area before and after treatment on the images which are acquired by the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). We calculated voxel-wise volume transport constant (Ktrans) using pharmacokinetic model, and designed a threshold d to get the volume fraction of voxels which Ktrans increased significantly (F+), Ktrans decreased significantly (F-) or had no significant change (F0). Linear regression analysis was performed to get the correlation between volume fractions and pathological tumor cell necrosis rate (TCNR). We then determined the ability of volume fractions to evaluate treatment response at early stage by receiver operating characteristic (ROC) curve analysis. We performed experiments on 10 patients with soft tissue sarcomas. The results indicated that F- had significant negative correlation with TCNR (R2=0.832 8, P=0.0002), F0 has significant positively correlation with TCNR (R2=0.788 4, P=0.0006). In addition, F-(AUC=0.905,P=0.053), F0 (AUC=0.857,P=0.087) had a good ability in early tumor treatment response evaluation. Therefore, F- and F0 can be used as effective imaging biomarkers for early evaluation of tumor treatment.
