Evaluation of the diagnostic value of YiDiXie™-SS, YiDiXie™-HS and YiDiXie™-D in gallbladder cancer
Zhou, H.; Zhang, P.; Sun, C.; Ge, Z.; Chen, W.; Li, Y.; Lin, S.; Wu, Y.; Wang, W.; Chen, S.; Li, X.; Li, W.; Lai, Y.
Show abstract
BackgroundGallbladder cancer is a grave threat to human health and poses a severe economic burden. Enhanced CT is extensively used in the diagnosis of gallbladder tumors. However, false-positive results on enhanced CT can lead to misdiagnosis and incorrect surgery or treatment, while false-negative results on enhanced CT can lead to missed diagnosis and delayed treatment. There is an urgency to find convenient, cost-effective and noninvasive diagnostic methods to decrease the false-positive and false-negative rates of gallbladder-enhanced CT. The goal of this study was to assess the diagnostic value of YiDiXie-SS, YiDiXie-HS and YiDiXie-D in gallbladder cancer. Patients and methodsFifty study subjects (malignant group, n=12; benign group, n=38 cases) were finally recruited in the study. Remaining serum samples from the subjects were collected and tested by applying YiDiXie all-cancer detection kit (YiDiXie all-cancer detection kit) to assess the sensitivity and specificity of YiDiXie-SS, YiDiXie-HS and YiDiXie-D, respectively. ResultsThe sensitivity of YiDiXie SS was 100% (95% CI: 75.8% - 100%) and its specificity was 65.8% (95% CI: 49.9% - 78.8%). This means that YiDiXie SS has very high sensitivity and high specificity in gallbladder tumors.The sensitivity of YiDiXie-HS was 83.3% (95% CI: 55.2% - 97.0%) and its specificity was 84.2% (95% CI: 69.6% - 92.6%). This means that YiDiXie-HS has high sensitivity and high specificity in gallbladder tumors.The sensitivity of YiDiXie-D was 66.7% (95% CI: 39.1% - 86.2%) and its specificity was 92.1% (95% CI: 79.2% - 97.3%). This means that YiDiXie -D has high sensitivity and very high specificity in gallbladder tumors. The sensitivity of YiDiXie-SS in patients with positive enhanced CT was 100% (95% CI: 64.6% - 100%), and its specificity was 60.0% (95% CI: 31.3% - 83.2%). It implies that the application of YiDiXie-SS reduces the false-positive rate of gallbladder-enhanced CT by 60.0% (95% CI: 31.3% - 83.2%) without essentially increasing the leakage of malignancies. The sensitivity of YiDiXie-HS in enhanced CT-negative patients was 80.0% (95% CI: 37.6% - 99.0%) and its specificity was 85.7% (95% CI: 68.5% - 94.3%). It implies that the application of YiDiXie -HS lowers the false-negative rate of enhanced CT by 80.0% (95% CI: 37.6% - 99.0%). The sensitivity of YiDiXie-D in patients with positive enhanced CT was 71.4%(95% CI: 35.9% -94.9%) and its specificity was 90.0%(95% CI: 59.6% - 99.5%). It implies that YiDiXie-SS lowers the false-positive rate of enhanced CT for 90.0%(95% CI: 59.6% - 99.5%). YiDiXie-D has a sensitivity of 60.0% (95% CI: 23.1% - 92.9%) in patients with negative enhanced CT and its specificity is 92.9% (95% CI: 77.4% - 98.7%). This means that YiDiXie-D reduces the false-negative rate of enhanced CT by 60.0% (95% CI: 23.1% - 92.9%) while maintaining high specificity. ConclusionYiDiXie -SS has very high sensitivity and high specificity in gallbladder tumors.YiDiXie -HS has high sensitivity and high specificity in gallbladder tumors.YiDiXie -D has high sensitivity and very high specificity in gallbladder tumors.YiDiXie -SS significantly reduced gallbladder enhanced CT false-positive rates with essentially no increase in delayed treatment for gallbladder cancer. YiDiXie -HS significantly reduces the false-negative rate of gallbladder enhanced CT. YiDiXie -D can significantly reduce the false-positive rate of gallbladder enhanced CT, or significantly reduce the false-negative rate of gallbladder enhanced CT while maintaining a high level of specificity. Clinical trial numberChiCTR2200066840.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The Role of Carbon Nanoparticle in Lymph Node Detection and Parathyroid Gland Protection during Thyroidectomy - a Meta Analysis 95%
- Machine learning based prediction of recurrence after curative resection for rectal cancer 95%
- Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images 95%
Similar papers in this journal
- DNMT family induced down-regulation of NDRG1 via DNA methylation and clinicopathological significance in gastric cancer 94%
- Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks 93%
- Ursolic acid improves the bacterial community mapping of the intestinal tract in liver fibrosis mice 92%
Similar papers in this journal
- Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging 93%
- Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques 93%
- Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications in Kyrgyzstan 92%
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 95%
- Post mortem pathological findings in COVID-19 cases: A Systematic Review 93%
- The Impact of Fasting the Holy Month of Ramadan on Colorectal Cancer Patients and Two Tumor Biomarkers: A Tertiary-Care Hospital Experience 93%
Similar papers in this journal
- Combined DeRitis ratio and alkaline phosphatase on the Prediction of Portal Vein Tumor Thrombosis in Patients with Hepatocellular Carcinoma 94%
- Immune Classification of Clear Cell Renal Cell Carcinoma 93%
- Effects of different environmental intervention durations on the intestinal mucosal barrier and the brain-gut axis in rats with colorectal cancer 93%
"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.