It was difficult to distinguish HAE from liver cancer because HAE had no specific clinical features, and imaging examination also lacked characteristic finding. The imaging findings of HAE and liver cancer are similar, such as a large space-occupying lesion in the liver, irregular areas of reduced density, etc14,15,16. Sometimes, liver cancer is difficult to distinguish from HAE in imaging, especially for those patients with low levels of AFP. Atypical liver cancer presents cystic lesion, no pseudocapsule, no dynamic scanning enhancement, and no portal vein invasion or tumor thrombus formation in imaging finds. The gold standard that diagnosed liver lesions is histopathological examination after surgical resection or preoperative fine needle aspiration biopsy. Therefore, a novel, non-invasive method is required to distinguish HAE from liver cancer before performing all kinds of therapies.
Platelet plays a crucial role in angiogenesis, shortening the time of wound healing, promoting liver regeneration, etc17. But, this is just one aspect of the PLT mechanism. When a tumor forms, tumor cells can disrupt the balance of the coagulation system by producing higher levels of coagulation factors. At the same time, the disorders of the coagulation system could also activate more levels of PLTs18,19. The activated PLTs provided the procoagulant surface to raise tumor-related gather. Tumor cells were surrounded by activated PLTs and then were escaped from the body’s immune detection, which promoted the growth and metastasis of the tumor. However, there is no such series of changes in the blood system and coagulation system during HAE formation. The PLT-based score models that based on blood system, coagulation system, and other factors is constructed in previous reports. But, they emphasized only the significance of recurrence and prognosis that associated with PLT-based score models. Therefore, we want to illustrate that the PLT-based score models are important factors for distinguishing HAE from liver cancer in our study.
To eliminate HBV interference and make the study more convincing, we include all patients with positive of HBsAg, HBeAg or HBeAb, and HBcAb. This model is widely applicable to patients with hepatitis B and non-hepatitis B. Since some factors could also cause fibrosis in HAE patients (not all liver cancer patients existed fibrosis), our study did not determine HAE or liver cancer solely based on the presence or absence of fibrosis. We use the level of fibrosis to comprehensively determine whether the patient is HAE or liver cancer. If a HAE patient with cirrhosis didn’t have a corresponding level of PLT-based score, there is a high probability that he or she is not liver cancer (the degree of fibrosis was not enough). Similarly, a liver cancer without cirrhosis (non-cirrhotic HCCs) had a high level of PLT-based score, there is a high probability that he or she is liver cancer.
Previous studies had only found that the possible risk factors were associated with the prognosis and diagnosis of disease, but it was not clear to what extent the factors were associated with the disease status. The nomogram is widely used as a predictive model in tumor and non-tumor diseases, and it could accurately predict disease status based on the degree of correlation between factors and disease20. Furthermore nomogram also provides tailored assessments of risk for specific patients and stratifies patients or patient groups by establishing risk thresholds for treatment decisions. That is something that other models don’t have.
We develops and validates a diagnostic, PLT-based score models nomogram for the preoperative individualized prediction of HAE and liver cancer. The nomogram incorporates four items of the PLT-based score models. Patients are successfully stratified by the histopathological results. Incorporating the PLT-based score models and PLT count into a user-friendly nomogram verifies the preoperative individualized predictive value of HAE and liver cancer. For developing the nomogram, 9 PLT-based score models factors are reduced to 4 possible indicators by LASSO analysis method that was used to reduce the regression coefficient to examine the correlation of the predicted results. Furthermore, 4 PLT-based score models selected by LASSO regression model are also evaluated by the binomial logistic regression analysis, and results show King’s score has an optimum diagnostic value compared with the other three factors. King’s score could independently predict HAE and liver cancer (p = 0.0388). But, we still choose to include three other factors because the AUC shows that API, FIB-4 and FibroQ also have a good diagnostic value. Furthermore, the AUC results also show that combining King’s score with three other factors (API, FIB-4 and FibroQ) in diagnosing HAE and liver cancer is of better value.
In recent studies, multi-biomarker analysis that incorporates a solitary biomarker into biomarker panels has been widely used21,22. For example, a 21-gene test identified and validates the avoidance of chemotherapy in patient with breast cancer23. Similarly, the predictive nomogram that incorporates PLT-based score models demonstrates a good discrimination in our research (C-index, 0.929). Furthermore, our simplified nomogram also showed a good identification capability in the validation group (C-index, 0.919). It is reported that the accuracy of diagnosis on preoperative peripheral blood tumor marker, such as carcinoembryonic antigen (CEA) and peripheral inflammatory factors, for lymph node metastasis of colorectal cancer was less than 70%, much lower than the C-index of PLT-based score models we constructed24. Therefore, the noninvasive, simplified prediction nomogram that incorporated the routine laboratory tests we already get for available, which could regard as a more simplified model for distinguishing HAE from liver cancer.
Both doctors and patients could also perform an individualized diagnosis model for predicting the probability of HAE and liver cancer with the easy feasible scoring system, which is in line with the current concept of precision medicine25. However, the clinical outcomes, the particular level of identification or degree of calibration, could not be got by the prediction capability, identification capability and calibration of nomogram26,27. The decision curve of our nomogram presents that if the threshold probability of an individual is more than 6%, using the differential diagnosis nomogram in our current research to distinguish HAE from liver cancer would get much more advantage than either distinguishing-all-patients or distinguishing-none.