Neutrophil–Lymphocyte Ratio and Absolute Lymphocyte Count as Prognostic Markers in Patients Treated with Curative-intent Radiotherapy for Non-small Cell Lung Cancer

Liu

Laryngoscope Investig Oto

et al. 2023

Objective(s)Absolute lymphocyte count (ALC) has been shown to be a prognostic indicator in other solid tumors. Given this, we aimed to evaluate the prognostic value of ALC in oral cavity squamous cell carcinoma (OSCC).MethodsUsing our institutional tumor registry data, we identified patients ≥18 years old who were diagnosed with OSCC between 2012 and 2018. Preoperative ALC values within 30 days of surgery were collected through retrospective chart review. American Joint Committee on Cancer, 7th‐edition best stage was used to categorize cancers as early stage (stages 1 and 2) or late‐stage (stages 3 and 4). Primary outcomes were likelihood of recurrence and survival rates after 3 years.ResultsOf the 412 patients identified, 262 patients had available ALC data and met inclusion criteria. Early stage cancer patients who had lymphopenia did not have any significant difference in their rate of death ([OR], 1.71, CI: 0.54–5.45, p = .36) or likelihood recurrence ([OR], 0.60, CI: 0.06–5.87, p = .66) after controlling for age, tobacco use, alcohol use, positive margins, and adjuvant therapy. Late‐stage cancer patients who had lymphopenia also showed no difference in their rate of death ([OR], 2.74, CI: 0.65–11.6, p = .17) or likelihood of recurrence ([OR], 0.38, CI: 0.04–3.36, p = .38).Conclusions and RelevanceThis study evaluates the prognostic value of ALC in oral cavity cancers. Our findings demonstrate that pretreatment ALC is not significantly associated with recurrence and survival outcomes patients with OSCC.Level of EvidenceIIILay SummaryAbsolute lymphocyte count (ALC) has been associated with prognosis in several cancers. We found that preoperative ALC was not associated with likelihood of survival or recurrence in patients with early stage or late‐stage oral cavity cancer.

Section: Introductionsupporting

confidence: 87%

Prognostic value of absolute lymphocyte count in oral cavity squamous cell carcinoma

Liu

Laryngoscope Investig Oto

et al. 2023

“…This result suggests that the reduction of peripheral blood lymphocyte count is one of the reasons for the poor prognosis of NSCLC patients, which was consistent with the previous findings. 13 However, most previous studies have evaluated the impact of lymphocyte count on distant progression and overall survival of lung cancer patients, few studies have studied the impact on the outcome of simple local recurrence. In this study, combined with preoperative ALC, early local recurrence after early NSCLC MWA was predicted.…”

Section: Discussionmentioning

confidence: 99%

“… 13–15 However, these studies investigated the evaluation of ALC in lung cancer patients who underwent resection 15 or chemoradiotherapy. 13 , 14 In addition, numerous studies in recent years have demonstrated that tumor ablation can induce anti-tumor immune responses, primarily by causing tumor cell necrosis, further antigen release, and then antigen presenting cells (APC) to present tumor antigens to effector cells that migrate to other parts of the disease and enhance the immune response. 16 This may also be a factor in the influence of pre-ablation lymphocyte count on the recurrence of lung cancer after ablation.…”

Section: Introductionmentioning

confidence: 99%

Nomogram Based on Preoperative Absolute Lymphocyte Count to Predict Local Recurrence in Patients with Non-Small Cell Lung Cancer After Microwave Ablation

Lv¹,

Liu²,

Li³

et al. 2023

JIR

Purpose This study aims to investigate the prognostic value of preoperative absolute lymphocyte count (preALC) for non-small cell lung cancer (NSCLC) after microwave ablation (MWA) and build a combined nomograph with clinical features to predict the local recurrence. Patients and Methods A total of 118 NSCLC patients who underwent microwave ablation were enrolled in this study. The median local recurrence-free survival (LRFS) was 35.5 months. Independent prognostic factors obtained by multivariate analysis were included in the prediction model. The prognostic value of the model was assessed by the area under the time-dependent receiver operating characteristic curve (T-AUC). Results Histological subtype and preALC were independent risk factors for local relapse-free survival. According to the time-dependent receiver operating characteristic curve (T-ROC), the optimal cut-off value of preALC was 1.965×10 9 /L, the sensitivity was 0.837, and the specificity was 0.594. The area under the T-ROC curve (AUC) of preALC was 0.703. To establish a nomogram to predict the local recurrence rate of NSCLC after MWA based on the prognostic factors revealed by Cox regression. Conclusion Preoperative lymphocyte count reduction is associated with poor prognosis of NSCLC. The nomogram model combined with preALC can provide a good individualized prediction of local recurrence after microwave ablation.

“…Tracking the changes during the treatment process could help to identify such features quickly [ 85 ]. Previous research has demonstrated that the alterations of NLR throughout treatment have a more reliable prognostic value for patients than NLR at a single point in time [ 79 , 119 ].…”

Section: Discussionmentioning

confidence: 99%

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

Tan

Zhao

et al. 2023

BMC Cancer

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.