Objective We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with hypopharyngeal squamous cell carcinoma (HSCC) treated with primary surgery to provide more accurate risk stratification for patients. Methods We retrospectively collected data of 1144 eligible patients with HSCC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Patients were randomly divided into training and validation groups (ratio 6:4) and we used univariate and multivariate Cox analysis. We developed and validated a nomogram using calibration plots and time-dependent receiver operating characteristic, Kaplan–Meier, and decision curves. Results Age; marital status; T, N, and M stage; and postoperative adjuvant therapy were independent factors associated with CSS, which were included in the nomogram. The nomogram’s C-index was 0.705 to 0.723 in the training group and 0.681 to 0.736 in the validation group, which were significantly higher than conventional American Joint Committee on Cancer (AJCC) staging. Calibration curves showed good agreement between prediction and observation in both groups. Kaplan–Meier and decision curves suggested the nomogram had better risk stratification and net benefit than conventional AJCC staging. Conclusions We established a nomogram that was superior to conventional AJCC staging in predicting CSS for HSCC.

Background  This study aimed to determine the prognostic value of preoperative blood parameters in renal cell carcinoma (RCC) and tumour thrombus (TT) patients that were surgically treated. Method We retrospectively analysed clinicopathological data and blood parameters of 146 RCC and TT patients that were surgically treated. Univariate or multivariate Cox regression analyses were performed to determine the risk factors associated with progression-free survival (PFS) and overall survival (OS). Kaplan-Meier analysis and logistic regression were performed to study the risk factors. Receiver operating characteristic curves were applied to test improvements in the predictive accuracy of the established prognosis score. Results On univariate and multivariate analysis, anaemia (HR 2.873, P = 0.008) and lymph node metastasis (HR 4.811, P = 0.015) were independent prognostic factors linked to OS. Besides, thrombocytosis (HR 2.324, P = 0.011), histologic subtype (HR 2.835, P = 0.004), nuclear grade (HR 2.069, P = 0.033), and lymph node metastasis (HR 5.739, P = 0.001) were independent prognostic factors associated with PFS. Kaplan–Meier curves revealed that patients with anaemia exhibited worse OS than those without it (P = 0.0033). Likewise, patients with thrombocytosis showed worse PFS than those without it (P < 0.0001). Adding the anaemia and thrombocytosis to the SSIGN score improved its predictive accuracy related to OS and PFS. Preoperative anaemia was linked to more symptom at presentation (OR 3.348, P = 0.006), longer surgical time (OR 1.005, P = 0.001), more blood loss (OR 1.000, P = 0.018), more transfusion (OR 2.734, P = 0.004), higher thrombus level (OR 4.750, P = 0.004) and higher nuclear grade (OR 3.449, P = 0.001) while thrombocytosis was associated with more symptom at presentation (OR 7.784, P = 0.007). Conclusions Preoperative anaemia and thrombocytosis were adverse prognostic factors in non-metastatic RCC patients with TT. Also, both preoperative anaemia and thrombocytosis can be clinically used for risk stratification of non-metastatic RCC and TT patients.

Background. Collecting duct renal cell carcinoma (CDRCC) is a rare type of renal cancer characterized by a poor prognosis. The aim of this work was to develop a nomogram predicting the overall survival (OS) and cancer-specific survival (CSS) for patients with CDRCC. Methods. A total of 324 eligible patients diagnosed with CDRCC from 2004 to 2015 were identified using the data from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier curve was used to estimate the 1-, 3-, and 5-year OS and CSS of these patients. Univariate and multivariate Cox regression models were performed to identify the independent risk factors associated with OS and CSS. The nomogram was developed based on these factors and evaluated by the concordance index (C-index) and calibration curves using the bootstrap resample method. The predictive accuracy of the nomogram was also compared with the manual of the American Joint Committee on Cancer (AJCC). Results. The estimated 1-, -3, and 5-year OS and CSS rates in the analytic cohorts were 56.4% and 60%, 32.5% and 37.3%, and 28.7% and 33.6%, respectively. The multivariate model revealed that age, tumor size, tumor grade, N stage, M stage, surgical type, and chemotherapy were independent predicted factors for OS, while tumor size, tumor grade, N stage, M stage, surgical type, and chemotherapy were independently linked to CSS. A nomogram was developed using these factors with relatively good discrimination and calibration. The C-index for OS and CSS was 0.764 (95% CI: 0.735~0.793) and 0.783 (95% CI: 0.754~0.812), which was superior to the AJCC stage (C-index: 0.685 (95% CI: 0.654~0.716) and 0.703 (95% CI: 0.672~0.734)). Patients were divided into low-risk, intermediate-risk, and high-risk groups according to the total points calculated by the nomogram. Patients in the low-risk group (97 mo and not reached) experienced significantly long median OS and CSS compared to the intermediate-risk (17 mo and 18 mo) and high-risk groups (5 mo for both). The calibration curves showed a good agreement between the predicted and actual probability related to OS and CSS. Conclusion. CDRCC has an aggressively biologic behavior with relatively poor prognosis. A survival prediction nomogram making an individualized evaluation of OS and CSS in patients with CDRCC was presented, potentially helping urologists to make a better risk stratification.

PurposeTumor-educated platelets (TEPs) are a promising liquid biopsy in many cancers. However, their role in renal cell carcinoma (RCC) is unknown. Thus, this study explored the diagnostic value of TEPs in RCC patients.MethodsPlatelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq was performed to identify the differentially expressed genes (DEGs) between RCC patients and controls. Besides, RNA-seq data of pan-cancer TEPs were downloaded and randomly divided into training and validation sets. A pan-cancer TEP model was developed in the training set using the support vector machine (SVM) and validated in the validation set and our RCC dataset. Finally, an RCC-based TEP model was developed and optimized through the SVM algorithms and recursive feature elimination (RFE) method.ResultTwo hundred three DEGs, 64 (31.5%) upregulated and 139 (68.5%) downregulated, were detected in the platelets of RCC patients compared with controls. The pan-cancer TEP model had a high accuracy in detecting cancer in the internal validation (training set, accuracy 98.8%, AUC: 0.999; validation set, accuracy 95.4%, AUC: 0.972; different tumor subtypes, accuracy 86.6%–96.1%, AUC: 0.952–1.000). However, the pan-cancer TEP model in the external validation had a scarce diagnostic value in RCC patients (accuracy 48.7%, AUC: 0.615). Therefore, to develop the RCC-based TEP model, the gene biomarkers mostly contributing to the model were selected using the RFE method. The RCC-based TEP model containing 68 gene biomarkers reached a diagnostic accuracy of 100% (AUC: 1.000) in the training set, 88.9% (AUC: 0.963) in the validation set, and 95.9% (AUC: 0.988) in the overall cohort.ConclusionTEPs could function as a minimally invasive blood biomarker in the detection of RCC.