Adjuvant chemotherapy for lymph node positive esophageal squamous cell cancer: The prediction role of low mean platelet volume

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Background

In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective

To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods

Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.

Results

RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.

Conclusion

We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

Relation of Mean Platelet Volume (MPV) with Cancer: A Systematic Review with a Focus on Disease Outcome on Twelve Types of Cancer

Inflammatory proteins activate platelets, which have been observed to be directly related to cancer progression and development. The aim of this systematic review is to investigate the possible association between Mean Platelet Volume (MPV) and cancer (diagnostic capacity of MPV, relation to survival, the severity of the disease, and metastasis). A literature review was performed in the online database PubMed and Google Scholar for the period of 2010–2022. In total, 83 studies including 21,034 participants with 12 different types of cancer (i.e., gastric cancer, colon cancer, esophageal squamous cell carcinoma, renal cancer, breast cancer, ovarian cancer, endometrial cancer, thyroid cancer, lung cancer, bladder cancer, gallbladder cancer, and multiple myeloma) were identified. The role of MPV has been extensively investigated in several types of cancer, such as gastric, colon, breast, and lung cancer, while few data exist for other types, such as renal, gallbladder cancer, and multiple myeloma. Most studies in gastric, breast, endometrium, thyroid, and lung cancer documented an elevated MPV in cancer patients. Data were less clear-cut for esophageal, ovarian, and colon cancer, while reduced MPV was observed in renal cell carcinoma and gallbladder cancer. Several studies on colon cancer (4 out of 6) and fewer on lung cancer (4 out of 10) indicated an unfavorable role of increased MPV regarding mortality. As far as other cancer types are concerned, fewer studies were conducted. MPV can be used as a potential biomarker in cancer diagnosis and could be a useful tool for the optimization of treatment strategies. Possible underlying mechanisms between cancer and MPV are discussed. However, further studies are needed to elucidate the exact role of MPV in cancer progression and metastasis.