Assessment of left atrioventricular coupling and left atrial function impairment in diabetes with and without hypertension using CMR feature tracking.

PURPOSE: The study was designed to assess the effect of co-occurrence of diabetes mellitus (DM) and hypertension on the deterioration of left atrioventricular coupling index (LACI) and left atrial (LA) function in comparison to individuals suffering from DM only. METHODS: From December 2015 to June 2022, we consecutively recruited patients with clinically diagnosed DM who underwent cardiac magnetic resonance (CMR) at our hospital. The study comprised a total of 176 patients with DM, who were divided into two groups based on their blood pressure status: 103 with hypertension (DM + HP) and 73 without hypertension (DM-HP). LA reservoir function (reservoir strain (εs), total LA ejection fraction (LAEF)), conduit function (conduit strain (εe), passive LAEF), booster-pump function (booster strain (εa) and active LAEF), LA volume index (LAVI), LV global longitudinal strain (LVGLS), and LACI were evaluated and compared between the two groups. RESULTS: After adjusting for age, sex, body surface area (BSA), and history of current smoking, total LAEF (61.16 ± 14.04 vs. 56.05 ± 12.72, p = 0.013) and active LAEF (43.98 ± 14.33 vs. 38.72 ± 13.51, p = 0.017) were lower, while passive LAEF (33.22 ± 14.11 vs. 31.28 ± 15.01, p = 0.807) remained unchanged in the DM + HP group compared to the DM-HP group. The DM + HP group had decreased εs (41.27 ± 18.89 vs. 33.41 ± 13.94, p = 0.006), εe (23.69 ± 12.96 vs. 18.90 ± 9.90, p = 0.037), εa (17.83 ± 8.09 vs. 14.93 ± 6.63, p = 0.019), and increased LACI (17.40±10.28 vs. 22.72±15.01, p = 0.049) when compared to the DM-HP group. In patients with DM, multivariate analysis revealed significant independent associations between LV GLS and εs (ß=-1.286, p < 0.001), εe (ß=-0.919, p < 0.001), and εa (ß=-0.324, p = 0.036). However, there was no significant association observed between LV GLS and LACI (ß=-0.003, p = 0.075). Additionally, hypertension was found to independently contribute to decreased εa (ß=-2.508, p = 0.027) and increased LACI in individuals with DM (ß = 0.05, p = 0.011). CONCLUSIONS: In DM patients, LV GLS showed a significant association with LA phasic strain. Hypertension was found to exacerbate the decline in LA booster strain and increase LACI in DM patients, indicating potential atrioventricular coupling index alterations.

Early evaluation of severe immune checkpoint inhibitor-associated myocarditis: a real-world clinical practice.

PURPOSE: Immune checkpoint inhibitor (ICI)-associated myocarditis is a rare but severe complication for patients treated with immunotherapy. This study aims to explore the predictive significance of patients' clinical features and examination results for the severity of ICI-associated myocarditis. METHODS: Data from a real-world cohort of 81 cancer patients who developed ICI-associated myocarditis after immunotherapy were retrospectively analyzed. The development of myocarditis of Common Terminology Criteria for Adverse Events (CTCAE) grades 3-5 and/or the major adverse cardiovascular event (MACE) was set as endpoints. Logistic regression was used to evaluate the predictive value of each factor. RESULTS: CTCAE grades 3-5 and MACE developed in 43/81 (53.1%) and 28/81 (34.6%) cases, respectively. The likelihood of CTCAE grades 3-5 and MACE increased with the accumulation of organs affected by the ICI-associated adverse events and initial clinical symptoms. Concurrent systematic therapies during ICI treatment did not raise the risk of myocarditis severity, while prior chemotherapy did. Besides classical serum cardiac markers, a higher neutrophil ratio was also related to poorer cardiac outcomes, whereas higher lymphocyte and monocyte ratios were predictors of favorable cardiac outcomes. The CD4+ T cell ratio and CD4/CD8 ratio were negatively related to CTCAE grades 3-5. Several cardiovascular magnetic resonance parameters were associated with myocarditis severity, whereas the predictive value of echocardiography and electrocardiogram was weak. CONCLUSION: This study comprehensively evaluated the prognostic value of patients' clinical characteristics and examination results and identified several predictors of severe ICI-associated myocarditis, which will facilitate early detection of severe ICI-associated myocarditis in patients receiving immunotherapy.

CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI.

RATIONALE AND OBJECTIVES: We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI). MATERIALS AND METHODS: A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760). CONCLUSION: This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.

Unsupervised machine learning based on clinical factors for the detection of coronary artery atherosclerosis in type 2 diabetes mellitus.

BACKGROUND: Coronary atherosclerosis can lead to serious cardiovascular events. In type 2 diabetes (T2DM) patients, the effects of clinical factors on coronary atherosclerosis have not been fully elucidated. We used a clustering method to distinguish the population heterogeneity of T2DM and the differences in coronary atherosclerosis evaluated on coronary computed tomography angiography (CCTA) among groups and to facilitate clinical management. METHODS: Clinical data from 1157 T2DM patients with coronary atherosclerosis who underwent CCTA in our hospital from January 2018 to September 2021 were retrospectively collected. The coronary artery segment plaque type and stenosis, the number of involved vessels, the segment involvement score (SIS) and the segment stenosis score (SSS) were evaluated and calculated. Unsupervised clustering analysis based on clinical information was used (cluster 1: n = 463; cluster 2: n = 341; cluster 3: n = 353). The association of coronary plaque characteristics with cluster groups was evaluated. RESULTS: The clinical data among the three groups were different in several aspects: (1) Cluster 1 had the least male patients (41.7%), the lowest proportion of patients with smoking (0%) or alcohol history (0.9%), and the lowest level of serum creatinine (74.46 ± 22.18 µmol/L); (2) Cluster 2 had the shortest duration of diabetes (7.90 ± 8.20 years) and was less likely to be treated with diabetes (42.2%) or statins (17.6%) and (3) Cluster 3 was the youngest (65.89 ± 10.15 years old) and had the highest proportion of male patients (96.6%), the highest proportion of patients with smoking (91.2%) and alcohol (59.8%) history, the highest level of eGFR (83.81 ± 19.06 ml/min/1.73m2), and the lowest level of HDL-C (1.07 ± 0.28 mmol/L). The CCTA characteristics varied with different clusters: (1) Cluster 1 had the largest number of segments with calcified plaques (2.43 ± 2.46) and the least number of segments with mixed plaques (2.24 ± 2.59) and obstructive stenosis (0.98 ± 2.00); (2) Cluster 1 had the lowest proportion of patients with mixed plaques (68%) and obstructive stenosis (32.2%); (3) Cluster 3 had more segments with noncalcified plaques than cluster 1 (0.63 ± 1.02 vs 0.40 ± 0.78, P < 0.05) and the highest proportion of patients with noncalcified plaques (39.9%) and (4) There was no significant difference in the extent of coronary plaques among the three clusters. CONCLUSIONS: The unsupervised clustering method could address T2DM patients with heterogeneous clinical indicators and identify groups with different types of coronary plaque and degrees of coronary stenosis. This method has the potential for patient stratification, which is essential for the clinical management of T2DM patients with coronary atherosclerosis.

Impact of Type 2 Diabetes Mellitus on Epicardial Adipose Tissue and Myocardial Microcirculation by MRI in Postmenopausal Women.

BACKGROUND: Type 2 diabetes mellitus (T2DM) often occurs conjunctly with the menopausal transition in female patients. In addition, epicardial adipose tissue (EAT) has an unfavorable impact on the myocardium and coronary arteries under the influence of metabolic disorders. PURPOSE: To investigate the impact of T2DM on EAT and myocardial microvascular function in postmenopausal women. STUDY TYPE: Retrospective. POPULATION: One-hundred sixty-one postmenopausal women divided into three groups: newly diagnosed (≤5 years) T2DM (n = 56, 58.6 ± 7.7 years), long-term (>5 years) T2DM (n = 57, 61.9 ± 7.9 years), and healthy controls (n = 48, 59.4 ± 7.4 years). FIELD STRENGTH/SEQUENCE: 3.0 T; balanced steady-state free precession and inversion recovery prepared echo-planar sequences. ASSESSMENT: EAT volume was quantified by delineating the epicardial border and the visceral layer of pericardium on the short-axis cine stacks. Perfusion parameters including upslope, maximum signal intensity (MaxSI) and time to maximum signal intensity (TTM) were derived from the first-pass perfusion signal intensity-time curves. STATISTICAL TESTS: One-way analysis of variance, Pearson's and Spearman correlation, and multivariable linear regression. Two-sided P < 0.05 was considered statistically significant. RESULTS: EAT volume was significantly increased in diabetic postmenopausal women compared to the controls (48.4 ± 13.4 mL/m2 [newly diagnosed T2DM] vs. 58.4 ± 17.3 mL/m2 [long-term T2DM] vs. 35.8 ± 12.3 mL/m2 [controls]). Regarding perfusion parameters, upslope and MaxSI were significantly reduced (2.6 ± 1.0 [newly diagnosed T2DM] vs. 2.1 ± 0.8 [long-term T2DM] vs. 3.6 ± 1.3 [controls]; and 21.4 ± 6.9 [newly diagnosed T2DM] vs. 18.7 ± 6.4 [long-term T2DM] vs. 28.4 ± 8.6 [controls]), whereas TTM was significantly increased in the T2DM groups compared to the control group (23.6 ± 8.7 [newly diagnosed T2DM] vs. 27.1 ± 9.4 [long-term T2DM] vs. 21.4 ± 6.0 [controls]). Multivariable analysis (adjusted coefficient of determination [R2 ] = 0.489) showed that EAT volume (ß = -0.610) and menopausal age (ß = 0.433) were independently correlated with decreased perfusion upslope. DATA CONCLUSION: Diabetic postmenopausal women had significantly higher EAT volume and more impaired microcirculation compared to the controls. Increased EAT volume and earlier menopausal age were independently associated with microvascular dysfunction in these patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

A comprehensive prognostic analysis of osimertinib treatment in advanced non-small cell lung cancer patients with acquired EGFR-T790M mutation: a real-world study.

PURPOSE: Osimertinib is the standard treatment for advanced non-small cell lung cancer (NSCLC) patients with T790M mutation after the failure of first-/second-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). We comprehensively analyzed factors that affect the therapeutic efficacy of the osimertinib treatment in NSCLC patients. METHODS: 351 NSCLC patients with T790M mutation receiving osimertinib treatment were included. We investigated the value of different factors in predicting the clinical outcomes of the osimertinib therapy, including progression-free survival (PFS), overall survival (OS) and objective response rate (ORR). Logistic and COX regression were used to identify prognosticators. RESULTS: In osimertinib therapy, EGFR mutation status (19Del/L858R) at initial diagnosis and the therapeutic choice of prior EGFR-TKI agent was not associated with patients' prognosis. Notably, the PFS of the prior EGFR-TKI was independently related to ORR (OR, 95% CI 0.98, 0.96-1.00, p = 0.030), PFS (HR, 95% CI 0.98, 0.97-1.00, p = 0.009) and OS (HR, 95% CI 0.96, 0.93-0.98, p < 0.001) of osimertinib treatment. Among distinct organ metastases, only bone metastasis was related to the efficacy of osimertinib, in terms of ORR (OR, 95% CI 1.97, 1.27-3.06, p = 0.002), PFS (HR, 95% CI 1.55, 1.18-2.03, p = 0.001) and OS (HR, 95% CI 1.81, 1.27-2.59, p = 0.001). However, the therapeutic efficacy of osimertinib was not further impacted by the accumulation of metastatic organs. A performance status score of 2-4 was also an adverse prognosticator for the osimertinib therapy. CONCLUSION: PFS of the prior EGFR-TKI treatment, performance status score and bone metastasis were independent prognosticators of the osimertinib treatment. These findings may facilitate clinicians in the decision-making of osimertinib.

Effect of Smoking on Coronary Artery Plaques in Type 2 Diabetes Mellitus: Evaluation With Coronary Computed Tomography Angiography.

Background: The effect of smoking on coronary artery plaques examined by coronary computed tomography angiography (CCTA) in type 2 diabetes mellitus (DM) patients is not fully understood. This study explored the effect of smoking on coronary artery plaques by comparing the characteristics of plaques between diabetes patients with and without a smoking history and among those with different smoking durations. Materials and Methods: In total, 1058 DM patients found to have coronary plaques on CCTA were categorized into the smoker (n=448) and nonsmoker groups (n=610). Smokers were stratified by smoking duration [≤20 years (n=115), 20~40 years (n=233) and >40 years (n=100)]. The plaque types, luminal stenosis [obstructive (<50%) or nonobstructive (≥50%) stenosis], segment involvement score (SIS), and segment stenosis score (SSS) of the CCTA data were compared among groups. Results: Compared to nonsmokers, smokers demonstrated increased odds ratios (ORs) of any noncalcified plaques (OR=1.423; P=0.014), obstructive plaques (OR=1.884; P<0.001), multivessel disease (OR=1.491; P=0.020), SIS≥4 (OR=1.662; P<0.001), and SSS≥7 (OR=1.562; P=0.001). Compared to diabetes patients with a smoking duration ≤20 years, those with a smoking duration of 20~40 years and >40 years had higher OR of any mixed plaques (OR=2.623 and 3.052, respectively; Ps<0.001), obstructive plaques (OR=2.004 and 2.098; P=0.003 and 0.008, respectively), multivessel disease (OR=3.171 and 3.784; P<0.001 and P=0.001, respectively), and SSS≥7 (OR=1.605 and 1.950; P=0.044 and 0.020, respectively). Diabetes with a smoking duration >40 years had a higher OR of SIS≥4 (OR=1.916, P=0.034). Conclusion: Smoking is independently associated with the presence of noncalcified, obstructive, and more extensive coronary artery plaques in diabetes patients, and a longer smoking duration is significantly associated with a higher risk of mixed, obstructive, and more extensive plaques.

Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment.

BACKGROUND AND PURPOSE: As a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy. METHODS: Contrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis. RESULTS: Disease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients' prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model. CONCLUSIONS: Radiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients' prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients' outcomes.

Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study.

BACKGROUND: Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features' value in predicting the TMB status of BLCA patients. METHODS: Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. RESULTS: The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. CONCLUSIONS: Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy.

BACKGROUND AND PURPOSE: Radiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown. METHODS: Computed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan-Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model's validation. RESULTS: Hierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn't distinguish patients' prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models. CONCLUSION: We firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

Distinguishing cardiac myxomas from cardiac thrombi by a radiomics signature based on cardiovascular contrast-enhanced computed tomography images.

BACKGROUND: Cardiac myxomas (CMs) and thrombi are associated with high morbidity and mortality. These two conditions need totally different treatments. However, they are difficult to distinguish using naked eye. In clinical, misdiagnoses occur now and then. This study aimed to compare the characteristics of CMs and cardiac thrombi and investigate the value of a radiomics signature in distinguishing CMs from cardiac thrombi, based on cardiovascular contrast-enhanced computed tomography (CECT) images. METHODS: A total of 109 patients who had CMs (n = 59) and cardiac thrombi (n = 50) were enrolled in this retrospective study from 2009 to 2019. First, the lesion characteristics of cardiovascular CECT images were documented and compared by two radiologists. Then all patients were randomly allotted to either a primary group or a validation group according to a 7:3 ratio. Univariate analysis and the least absolute shrinkage and selection operator were used to select robust features. The best radiomics signature was constructed and validated using multivariate logistic regression. An independent clinical model was created for comparison. RESULTS: The best radiomics signature was developed using eight selected radiomics. The classification accuracies of the radiomics signature were 90.8% and 90.9%, and the area under the receiver operating characteristic curves were 0.969 and 0.926 in the training and testing cohorts, respectively. Cardiovascular CECT images showed that the two diseases had significant differences in location, surface, Hydrothorax, pericardial effusion and heart enlargement. The naked eye findings were used to create the clinical model. All metrics of the radiomics signature were higher than those of clinical model. CONCLUSIONS: Compared with clinical model, the radiomics signature based on cardiovascular CECT performed better in differentiating CMs and thrombi, suggesting that it could help improving the diagnostic efficiency.

A novel prognostic model predicting the long-term cancer-specific survival for patients with hypopharyngeal squamous cell carcinoma.

BACKGROUND: Hypopharyngeal squamous cell carcinoma (HSCC) is a rare type of head and neck cancer with poor prognosis. However, till now, there is still no model predicting the survival outcomes for HSCC patients. We aim to develop a novel nomogram predicting the long-term cancer-specific survival (CSS) for patients with HSCC and establish a prognostic classification system. METHODS: Data of 2021 eligible HSCC patients were retrieved from the Surveillance, Epidemiology and End Results database between 2010 and 2015. We randomly split the whole cases (ratio: 7:3) into the training and the validation cohort. Cox regression as well as the Least absolute shrinkage and selection operator (LASSO) COX were used to select significant predictors of CSS. Based on the beta-value of these predictors, a novel nomogram was built. The concordance index (C-index), the calibration curve and the decision curve analysis (DCA) were utilized for the model validation and evaluation using the validation cohort. RESULTS: In total, cancer-specific death occurred in 974/2021 (48.2%) patients. LASSO COX indicated that age, race, T stage, N stage, M stage, surgery, radiotherapy and chemotherapy are significant prognosticators of CSS. A prognostic model based on these factors was constructed and visually presented as nomogram. The C-index of the model was 0.764, indicating great predictive accuracy. Additionally, DCA and calibration curves also demonstrated that the nomogram had good clinical effect and satisfactory consistency between the predictive CSS and actual observation. Furthermore, we developed a prognostic classification system that divides HSCC patients into three groups with different prognosis. The median CSS for HSCC patients in the favorable, intermediate and poor prognosis group was not reached, 39.0-Mo and 10.0-Mo, respectively (p < 0.001). CONCLUSIONS: In this study, we constructed the first nomogram as well as a relevant prognostic classification system that predicts CSS for HSCC patients. We believe these tools would be helpful for clinical practice in patients' consultation and risk group stratification.