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Background: Malignant pleural mesothelioma (MPM) is a rare but aggressive tumor that originates from the pleura and has a poor prognosis. Eligible patients can benefit from surgery, but their survival is affected by many factors. Therefore, we created a graphic model that could predict the prognosis of surgically treated patients. Methods: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results database from 2004 to 2014 to identify the key factors affecting the prognosis of surgically treated MPM patients. On this basis we built a nomogram to predict survival. We then evaluated the performance of the nomogram in a validation cohort. Results: In a training cohort of 828 cases, independent prognostic factors, including age, gender, histological type, differentiation, N stage, chemotherapy, type of surgery, and lymph node dissection, were identified. We then developed a nomogram to evaluate individual patient survival. In Kaplan–Meier analysis, a higher score in the nomogram was associated with a worse prognosis. We also used a validation cohort consisting of 312 patients to evaluate the performance of the nomogram, which was well calibrated and had good discrimination ability, with concordance indices of 0.715 and 0.656 for the training and validation cohorts, respectively. Conclusion: This study has improved our understanding of resected MPM and shown that key factors, including age and histological type, are associated with overall survival. The nomogram is a reliable tool that can help clinicians turn individualized prediction into reality and maximize patient benefit by identifying the most beneficial treatment approach. [ABSTRACT FROM AUTHOR]

Background: Gemcitabine plus cisplatin (GP) is commonly used to treat lung squamous cell carcinoma (SCC); however, it is not clear which subgroup of lung SCC patients could benefit most from GP treatment. We explored the predictive factors in lung SCC patient cohorts. Methods: Seventy‐eight lung SCC patients treated with a first‐line GP regimen were enrolled in this retrospective cohort study. Progression‐free survival (PFS) and overall survival (OS) were estimated using the Kaplan–Meier method. Classification tree models were used to explore the risk factors for PFS and OS in these patients. Results: The median PFS and OS in SCC patients treated with a GP regimen were 6.0 and 13.6 months, respectively. Three terminal subgroups were formed for both PFS and OS. The subgroup with a body mass index (BMI) > 23.94 kg/m2 and aged ≤ 54.5 had the longest PFS (9.0 months); the subgroup with a BMI < 23.94  kg/m2 and aged ≤ 54.5 had the shortest PFS (4.05 months). Patients with an objective response (partial or complete response) to treatment had the longest OS (20.0 months), while patients with a BMI ≤ 26.92 kg/m2 and stable or progressive disease as the best response had the shortest OS (11.2 months). Conclusions: BMI and age may be predictors of PFS in lung SCC patients who receive GP treatment. BMI and best response to GP treatment predicts OS in such patients. Patients’ clinical pathological characteristics may be used to predict the therapeutic efficacy of chemotherapy and survival. [ABSTRACT FROM AUTHOR]