PURPOSE: We aimed to evaluate the efficacy and feasibility of patient-reported outcome (PRO)-based symptom management in the early period after lung cancer surgery. METHODS: Before surgery, patients with clinically diagnosed lung cancer were randomly assigned 1:1 to receive postoperative PRO-based symptom management or usual care. All patients reported symptoms on MD Anderson Symptom Inventory-Lung Cancer presurgery, daily postsurgery, and twice a week after discharge for up to 4 weeks via an electronic PRO system. In the intervention group, treating surgeons responded to overthreshold electronic alerts driven by any of the five target symptom scores (score ≥ 4 on a 0-10 scale for pain, fatigue, disturbed sleep, shortness of breath, and coughing). The control group patients received usual care and no alerts were generated. The primary outcome was the number of symptom threshold events (any target symptom with a score of ≥ 4) at discharge. Per-protocol analyses were conducted. RESULTS: Of the 166 participants, 83 were randomly allocated to each group. At discharge, the intervention group reported fewer symptom threshold events than the control group (median [interquartile range], 0 [0-2] v 2 [0-3]; P = .007). At 4 weeks postdischarge, this difference was maintained between the intervention and control groups (median [interquartile range], 0 [0-0] v 0 [0-1]; P = .018). The intervention group had a lower complication rate than the control group (21.5% v 40.6%; P = .019). Surgeons spent a median of 3 minutes managing an alert. CONCLUSION: PRO-based symptom management after lung cancer surgery showed lower symptom burden and fewer complications than usual care for up to 4 weeks postdischarge.

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.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. We previously reported superior symptom control of electronic patient-reported outcome (ePRO)–based symptom management after lung cancer surgery for up to 1 month postdischarge. Here, we present the long-term results (1-12 months) of this multicenter, randomized trial, where patients were assigned 1:1 to receive postoperative ePRO-based symptom management or usual care daily postsurgery, twice weekly postdischarge until 1 month, and at 3, 6, 9, and 12 months postdischarge. Long-term patient-reported outcomes were assessed with MD Anderson Symptom Inventory-Lung Cancer module. Per-protocol analyses were performed with 55 patients in the ePRO group and 57 in the usual care group. At 12 months postdischarge, the ePRO group reported significantly fewer symptom threshold events (any of the five target symptom scored ≥4; median [IQR], 0 [0-0] v 0 [0-1]; P = .040) than the usual care group. From 1 to 12 months postdischarge, the ePRO group consistently reported significantly lower composite scores for physical interference (estimate, –0.86 [95% CI, –1.32 to –0.39]) and affective interference (estimate, –0.70 [95% CI, –1.14 to –0.26]). Early intensive ePRO-based symptom management after lung cancer surgery reduced symptom burden and improved functional status for up to 1 year postdischarge, supporting its integration into standard care.

Programmed death‐ligand 1 (PD‐L1) expression either indicates immune inhibitory status or concurrent immune response. Although the relationship between PD‐L1 and clinical outcomes has been studied widely in recent years, its role in prognosis of esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we assessed the significance of PD‐L1 in ESCC and its association with epidermal growth factor receptor (EGFR) and radiation response. We found that PD‐L1 was present both on the surface of tumor cells and tumor‐infiltrating immune cells. Patients with tumor‐infiltrating immune cell PD‐L1 expression had better survival. PD‐L1 expression on immune cells was an independent prognostic factor for patients with ESCC. PD‐L1 expression either on tumor‐infiltrating immune cells or tumor cells was negatively associated with EGFR expression. EGFR/PD‐L1 pairs could separate the survival between EGFR low/PD‐L1 positive and EGFR high/PD‐L1 negative groups. In ESCC cell lines with EGFR high expression, PD‐L1 expression was induced significantly when EGFR signaling was activated by radiation and was dramatically inhibited by an EGFR tyrosine kinase inhibitor. In conclusion, tumor‐infiltrating immune cell PD‐L1 expression is an independent prognostic factor for ESCC, and the association between EGFR and PD‐L1 is vital to determining survival. It is important to consider radiotherapy‐induced imbalance of pro‐tumor and anti‐tumor immune response. A combination of radiotherapy and PD‐L1‐targeted therapy could be a promising therapeutic strategy for ESCC patients.

Background: The overall survival of patients with esophageal squamous cell carcinoma (ESCC) remains poor. Prognostic predictions in ESCC are usually based on histological assessment of tumor invasion and lymph node metastasis, but a biomarker with better predictive accuracy could be more useful. Because overexpression of epidermal growth factor receptor (EGFR) has been associated with poor prognosis, this study investigated whether EGFR is an independent prognostic factor for overall survival and disease-free survival of ESCC patients. Methods: ESCC tissue specimens from 243 patients obtained during surgical resection between 1980 and 1997 were retrieved for immunohistochemical analysis of EGFR expression. Results: The data showed that EGFR protein was overexpressed in 187 of 243 (77%) ESCC tissues. Elevated expression was associated with higher pathologic tumor stages (P = 0.001), lymph node metastasis (P = 0.002), and higher Union for International Cancer Control (UICC) stage (P <0.0001), as well as poorer disease-free survival and overall survival of ESCC patients (P <0.0001). A multivariate analysis showed that overexpression of EGFR protein was an independent factor for disease-free survival (P = 0.003) and overall survival (P = 0.001) of these patients. Subgroup analysis of patients with stage IIA (UICC 2002) showed that EGFR overexpression was associated with poorer disease-free survival (P = 0.007) and overall survival (P = 0.010) of the patients in univariate analyses. Conclusions: The current study demonstrated that EGFR overexpression was an independent prognostic factor for overall survival and disease-free survival of ESCC patients. However, targeting of EGFR activity using gefitinib or erlotinib could be useful for clinical treatment of ESCC patients.