Evaluation of the diagnostic value of YiDiXie™-SS, YiDiXie™-HS and YiDiXie™-D in brain malignant tumors
Wu, Y.; Sun, C.; Ge, Z.; Zhou, H.; Li, X.; Chen, W.; Li, Y.; Lin, S.; Zhang, P.; Wang, W.; Chen, S.; Li, W.; Hu, J.; Ji, L.; Lai, Y.
Show abstract
BackgroundBrain malignant tumors is a serious threat to human health and causes heavy economic burden. Enhanced MRI is widely used in the diagnosis of brain tumors. However, false-positive results of enhanced MRI will lead to misdiagnosis and incorrect surgery or treatment, while false-negative results of enhanced MRI will lead to underdiagnosis of malignant tumors and delayed treatment. There is an urgent need to find convenient, cost-effective and non-invasive diagnostic methods to reduce the false-positive and false-negative rates of brain-enhanced MRI. The aim of this study was to evaluate the diagnostic value of YiDiXie-SS, YiDiXie-HS and YiDiXie-D in brain malignant tumors. Patients and methods233 subjects (malignant group, n=74; benign group, n=159) were finally included in this study. Remaining serum samples from the subjects were collected and tested by applying the YiDiXie all-cancer detection kit to evaluate the sensitivity and specificity of YiDiXie-SS, YiDiXie-HS and YiDiXie-D. ResultsThe sensitivity of YiDiXie SS was 97.3% (95% CI: 90.7% - 99.5%) and its specificity was 63.5% (95% CI: 55.8% - 70.6%). This means that YiDiXie -SS has a very high sensitivity and specificity in brain tumors.YiDiXie -HS has a sensitivity of 83.8% (95% CI: 73.8% - 90.5%) and a specificity of 84.9% (95% CI: 78.5% - 89.6%). This means that YiDiXie-HS has high sensitivity and specificity in brain tumors.YiDiXie-D has a sensitivity of 70.3% (95% CI: 59.1% - 79.5%) and a specificity of 93.7% (95% CI: 88.8% - 96.5%). This means that YiDiXie-D has high sensitivity and very high specificity in brain tumors. The sensitivity of YiDiXie SS in patients with positive enhancement MRI was 98.3% (95% CI: 91.1% - 99.9%) and its specificity was 62.5% (95% CI: 38.6% - 81.5%). This means that the application of YiDiXie -SS reduces the false-positive rate of enhanced MRI by 62.5% (95% CI: 38.6% - 81.5%) with essentially no increase in the leakage of malignant tumors.The sensitivity of YiDiXie-HS in patients with negative enhanced MRI was 85.7% (95% CI: 60.1% - 97.5%) and its specificity was 84.6% (95% CI: 77.8% - 89.6%). This means that the application of YiDiXie -HS reduced the false-negative rate of enhanced MRI by 85.7% (95% CI: 60.1% - 97.5%). YiDiXie-D had a sensitivity of 71.7% (95% CI: 59.2% - 81.5%) and a specificity of 93.8% (95% CI: 71.7% - 99.7%) in patients with positive enhanced MRI. This means that YiDiXie -D reduced the false-positive brain enhanced MRI rate by 93.8% (95% CI: 71.7% - 99.7%). YiDiXie-D had a sensitivity of 64.3% (95% CI: 38.8% - 83.7%) and a specificity of 93.7% (95% CI: 88.5% - 96.7%) in patients with negative enhanced MRI. This means that YiDiXie -D reduces the false-negative rate of enhanced MRI by 64.3% (95% CI: 38.8% - 83.7%) while maintaining high specificity. ConclusionYiDiXie -SS has extremely high sensitivity and high specificity in brain tumors.YiDiXie -HS has high sensitivity and high specificity in brain tumors.YiDiXie -D has high sensitivity and extremely high specificity in brain tumors.YiDiXie-SS significantly reduces the false-positive rate of brain-enhanced MRIs with essentially no increase in delayed treatment of malignant tumors. The YiDiXie-HS significantly reduces the false-negative rate of brain-enhanced MRI. the YiDiXie-D can significantly reduce the false-positive rate of brain-enhanced MRI or significantly reduce the false-negative rate of brain-enhanced MRI while maintaining a high level of specificity. The YiDiXie test has significant diagnostic value in brain tumors, and is expected to solve the problems of " high false-positive rate " and " high false-negative rate" of brain-enhanced MRI. Clinical trial numberChiCTR2200066840.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images 93%
- Machine learning based prediction of recurrence after curative resection for rectal cancer 93%
- Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study 93%
Similar papers in this journal
- Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques 93%
- Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging 92%
- Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of Hepatocellular carcinoma through transcriptome analysis 91%
Similar papers in this journal
- Association of Graph-based Spatial Features with Overall Survival Status of Glioblastoma Patients 94%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 93%
- Combined DeRitis ratio and alkaline phosphatase on the Prediction of Portal Vein Tumor Thrombosis in Patients with Hepatocellular Carcinoma 93%
Similar papers in this journal
- Effects of contrast-medium and vertebral measurement level on computed tomography-based body composition parameters of skeletal muscle and adipose tissue 94%
- Post mortem pathological findings in COVID-19 cases: A Systematic Review 91%
- Benchmarking Deep Learning-based Image Retrieval of Oral Tumor Histology 91%
Similar papers in this journal
- Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides 92%
- miR-100-5p downregulates mTOR to suppress the proliferation, migration and invasion of prostate cancer cells 91%
- Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.