Back

Evaluation of the diagnostic value of YiDiXie™-HS, YiDiXie™-SS and YiDiXie™-D in liver tumors

Zhou, H.; Sun, C.; Chen, S.; Wu, Y.; Li, X.; Ge, Z.; Chen, W.; Li, Y.; Lin, S.; Zhang, P.; Wang, W.; Li, W.; Sun, X.; Ji, L.; Li, J.; Lai, Y.

2024-07-16 oncology
10.1101/2024.07.15.24310471 medRxiv
Show abstract

BackgroundLiver cancer is one of the cancers that consistently ranks among the top five cancers in terms of incidence and mortality in many countries. 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 urgent need to find convenient, cost-effective and non-invasive diagnostic methods to reduce the false-positive rate of ultrasound and the false-negative and false-positive rates of enhanced CT for liver tumors. The purpose of this study is to evaluate the diagnostic value of YiDiXie-HS, YiDiXie-SS and YiDiXie-D in liver tumors. Patients and methodsThis study finally included 217 subjects (the malignant group, n=185; the benign group, n=32). Remaining serum samples from the subjects were collected and tested using the YiDiXie all-cancer detection kit, which was applied to assess the sensitivity and specificity of YiDiXie-SS, YiDiXie-HS and YiDiXie-D, respectively. ResultsThe sensitivity of YiDiXie-SS was 98.9% (96.1% - 99.8%) and its specificity was 68.8% (51.4% - 82.0%). This means that YiDiXie-SS has very high sensitivity and high specificity in liver tumors.YiDiXie-HS has a sensitivity of 88.1% (82.7% - 92.0%) and its specificity is 84.4% (68.2% - 93.1%). This means that YiDiXie-HS has high sensitivity and high specificity in liver tumors.YiDiXie-D has a sensitivity of 72.4% (65.6% - 78.4%) and its specificity is 93.8% (79.9% - 98.9%). This means that YiDiXie-D has high sensitivity and very high specificity in liver tumors.YiDiXie-SS has a sensitivity of 99.2% (95.8% - 100%) and a specificity of 66.7% (39.1% - 86.2%) in patients with positive enhanced CT. This means that the application of YiDiXie-SS reduces the false-positive rate of enhanced CT by 66.7% (39.1% - 86.2%) with essentially no increase in the leakage of malignant tumors.YiDiXie-HS has a sensitivity of 89.1% (95% CI: 78.2% - 94.9%) in patients with a negative enhanced CT, and its specificity is 85.0% (64.0% - 94.8%). This means that YiDiXie-HS reduces the false-negative rate of enhanced CT by 85.0% (64.0% - 94.8%). YiDiXie-D has a sensitivity of 73.1% (64.9% - 80.0%) and a specificity of 91.7% (64.6% - 99.6%) in patients with positive enhanced CT. This means that YiDiXie-D reduces the false-positive rate of enhanced CT by 91.7% (64.6% - 99.6%). YiDiXie-D has a sensitivity of 70.9% (95% CI: 57.9% - 81.2%) and a specificity of 95.0% (95% CI: 76.4% - 99.7%) in patients with negative enhanced CT. This means that YiDiXie -D reduces the false-negative rate of enhanced CT by 70.9% (95% CI: 57.9% - 81.2%) while maintaining high specificity. ConclusionYiDiXie-SS has very high sensitivity and high specificity in liver tumors. YiDiXie-HS has high sensitivity and high specificity in liver tumors. YiDiXie-D has high sensitivity and very high specificity in liver tumors. YiDiXie-SS significantly reduces the false positive rate of liver-enhanced CT with essentially no increase in delayed treatment of malignant tumors. YiDiXie-HS significantly reduces the false-negative rate of enhanced CT. YiDiXie-D can significantly reduce the false-positive rate of enhanced CT or significantly reduce the false-negative rate of enhanced CT while maintaining a high specificity. The YiDiXie test has significant diagnostic value in liver tumors, and is expected to solve the problems of "high false-positive rate" and "high false-negative rate" of enhanced CT in liver tumors. Clinical trial numberChiCTR2200066840.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.