The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

Abstract

Background Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. Methods Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set’s prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. Results From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. Conclusions This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Characterization of blood inflammatory markers in patients with non-small cell lung cancer

“…LYM, HGB, albumin, and MLR were all lower in the NSCLC group than in the control group (all P<0.05). This is similar to the results of other studies on the correlation between inflammation and tumor [5,[7][8][9], providing further evidence that NSCLC is closely related to inflammation.…”

“…There was no statistical significance in age and sex (P>0.05), and the two groups were comparable, as shown in Table 3. Int J Clin Exp Pathol 2024;17 (5):165-172…”

“…NSCLC is often detected late, and clinical treatment for NSCLC is limited and there is a lack of effective methods for early detection. Most patients are in the middle and late stages when they are found, with a low 5-year survival rate and a high mortality rate [5]. With ongoing research into the pathogenesis of malignant tumors, it has been found that inflammatory markers can reflect the immune and nutritional status of the body.…”

“…There are few studies on the correlation between clinicopathologic features of NSCLC and inflammatory indicators. In this study, we retrospectively compared the blood inflammatory markers of NSCLC patients and healthy people, and the changes in inflammatory markers in NSCLC patients based on different clini-Int J Clin Exp Pathol 2024;17 (5):165-172 copathological features. We investigated the relationship between NSCLC and blood inflammatory markers, and explored the clinical application value of inflammatory markers with regard to diagnosis and prognosis.…”

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review

“…However, these diverse datasets often comprise a substantial number of features. Some studies have noted overfitting in their models due to the utilization of a larger number of features relative to a smaller sample size [66,96]. This issue is commonly referred to as the 'n << P problem,' where 'n' represents the sample size and 'P' denotes the number of features [102].…”