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

A new perspective of blood routine test for the prediction and diagnosis of hyperglycemia

BACKGROUND AND AIMS

The presence of hyperglycemia induces alterations in the blood cell microenvironment. However, further investigations are warranted to comprehend the regulation of physiological parameter changes within the hyperglycemic cohort and validate their correlation. This study aims to investigate the correlation between hyperglycemia and peripheral blood physiological parameters, providing valuable insights for the screening and treatment of hyperglycemia.

METHODS

A retrospective study was conducted to analyze the demographic characteristics and blood routine test (blood RT) results of both the normal population and individuals with hyperglycemia. The distribution of abnormal blood RT results was compared between the hyperglycemic groups and the normal group. Univariate and multivariate logistic regression analyses were employed to investigate the correlation between blood RT results and levels of hyperglycemia. In addition, the stored red blood cells (RBCs) were placed in high glucose concentration and low glucose concentration environment, and the changes of physiological parameters of RBCs were observed after 35 days of storage.

RESULTS

The study included a total of 413 participants, with 202 individuals representing the normal population. Among these, there were 95 males (47.03%) and 107 females (52.97%). The hyperglycemia group consisted of individuals with impaired glucose tolerance (IGT) and diabetes mellitus (DM). Out of the total sample, 61 participants with IGT, consisting of 45 males (73.77%) and 16 females (26.23%). Additionally, there were 150 participants with DM, including 107 males (71.33%) and 43 females (28.67%). The prevalence of hyperglycemia showed a significant increase among males aged over 45 years (p < 0.05). The levels of white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC) in the hyperglycemia group were significantly higher than those in the normal group (p < 0.05). The distribution of abnormal blood RT results revealed that the DM group had the highest proportion of abnormal WBC, while the IGT group exhibited the highest proportions of abnormal RBC, HGB, and HCT (p < 0.05). Univariate logistic regression analysis showed that WBC (odds ratio [OR], 1.422; 95% CI, 1.249-1.631), RBC (OR, 2.163; 95% CI, 1.449-3.270), HGB (OR, 1.033; 95% CI, 1.020-1.047), HCT (OR, 4.549; 95% CI, 0.569-8.591), MCH (OR, 1.175; 95% CI, 1.057-1.319), MCHC (OR, 1.071; 95% CI, 1.047-1.098) were the predictor indices for hyperglycemia (p < 0.05). Multivariate logistic regression analysis showed that WBC (OR, 1.434; 95% CI, 1.193-1.742) and MCHC (OR, 4.448; 95% CI, 0.084-237.9) were predictor indices for hyperglycemia (p < 0.05). The results of in vitro experiments demonstrated that the high glucose concentration significantly decreased MCV, while concurrently increasing MCHC and coefficient variation of the distribution width of the red blood cell (RDW-CV) (p < 0.05).

CONCLUSION

The present study revealed significant correlations between hyperglycemia and gender, age, as well as certain peripheral blood physiological parameters. Moreover, in vitro experiments provided further support for these associations. Consequently, peripheral blood physiological parameters can serve as valuable predictor indices for DM and IGT prevention, offering essential insights to enhance preventive strategies.

Adult patients with Philadelphia chromosome positive acute lymphoblastic leukemia undergoing allogeneic hematopoietic stem cell transplantation and tyrosine kinase inhibitors: development and validation of a clinical prediction model based on cytogenetics, IKZF1 deletions and minimal residual disease

The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(n = 124) and 30% of the patients were assigned to the validation group(n = 52). Univariate Cox regression analysis and Akaike Information Criterion(AIC) were utilized to identify significant predictive factors, leading to the development of a nomogram designed to forecast the probability of PFS at 6, 9, and 12 months post-transplantation. The final nomogram incorporated three key variables: presence of complex additional cytogenetic abnormalities (ACAs), minimal residual disease (MRD) status prior to allo-HSCT, and IKZF1 gene deletions. The calibration curves showed excellent consistency between the nomogram prediction and actual observation for 6-, 9- and 12-month PFS in the training set and validation set. The C-index of the training set was 0.726(95%CI: 0.635-0.816), which was no significantly different from the validation set(C-index = 0.774, 95%CI: 0.674-0.875, P > 0.05). This study may provide a simple and efficient prediction model for patients with Ph + ALL undergoing allo-HSCT and TKIs, which can accurately predict PFS subsequent to transplantation. This tool could potentially aid clinicians in decision-making processes and improve patient outcomes.

Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators

Introduction

Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM.

Methods

This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy.

Results

The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers.

Conclusions

The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

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

OBJECTIVE

To investigate the differences and correlation between blood inflammatory indexes such as monocytes (MONO), lymphocytes (LYM), haemoglobin (HGB), neutrophils (NEU), platelets (PLT), ultrasensitive C-reactive protein, albumin and platelet/lymphocyte ratio (PLR), NEU/LYM ratio (NLR), MONO/LYM ratio (MLR) and clinicopathologic characteristics of patients with non-small cell lung cancer (NSCLC).

METHODS

187 patients with NSCLC who were first diagnosed in 2017-2023 and 102 with healthy check-ups during the same period (control group) were retrospectively selected as study subjects to compare the differences in inflammatory indexes between the two groups and the levels of inflammatory indexes in NSCLC patients with different clinicopathologic characteristics.

RESULTS

Correlation analysis between blood inflammatory indexes and clinicopathologic features in NSCLC group showed that C-reactive protein, CAR, and PLR values were different in different pathologic types (P<0.05). The values of NEU, MONO, C-reactive protein, MLR, NLR, CAR and albumin were different among various degrees of differentiation (P<0.05). There were differences in LYM, albumin, MLR, NLR, CAR, and C-reactive protein among M stage subgroups (P<0.05). Analysis of the efficacy of early diagnosis of non-small cell lung cancer has been shown, the AUC of NLR was 0.796, sensitivity of 0.679, specificity of 0.176, 95% CI=0.743-0.849 (P<0.001). The AUC of albumin was 0.977, the sensitivity was 0.941, the specificity was 0.941, and 95% CI was 0.959-0.994 (P<0.001).

CONCLUSION

Blood inflammatory indexes are closely associated with NSCLC and vary according to pathologic features. Blood inflammatory indices can predict tumor pathologic staging and guide treatment for patients with NSCLC.