Background and PurposeAs 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.MethodsContrast-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.ResultsDisease 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.ConclusionsRadiomic 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.

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.

Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with sex-specific pathophysiology. Estrogen deficiency is believed to be responsible for the development of HFpEF in women. However, estrogen deficiency does not seem to be completely responsible for the differences in HFpEF prevalence between sexes. While diabetes mellitus (DM) frequently coexists with HFpEF in women and is associated with worse outcomes, the changes in myocardial contractility among women with HFpEF and the DM phenotype is yet unknown. Therefore, we aimed to investigate sex-related differences in left ventricular (LV) contractility dysfunction in HFpEF comorbid with DM. Methods A total of 224 patients who underwent cardiac cine MRI were included in this study. Sex-specific differences in LV structure and function in the context of DM were determined. LV systolic strains (global longitudinal strain [GLS], circumferential strain [GCS] and radial strain [GRS]) were measured using cine MRI. The determinants of impaired myocardial strain for women and men were assessed. Results The prevalence of DM did not differ between sexes (p > 0.05). Despite a similar LV ejection fraction, women with DM demonstrated a greater LV mass index than women without DM (p = 0.023). The prevalence of LV geometry patterns by sex did not differ in the non-DM subgroup, but there was a trend toward a more abnormal LV geometry in women with DM (p = 0.072). The magnitudes of systolic strains were similar between sexes in the non-DM group (p > 0.05). Nevertheless, in the DM subgroup, there was significant impairment in women in systolic strains compared with men (p < 0.05). In the multivariable analysis, DM was associated with impaired systolic strains in women (GLS [β = 0.26; p = 0.007], GCS [β = 0.31; p < 0.001], and GRS [β = −0.24; p = 0.016]), whereas obesity and coronary artery disease were associated with impaired systolic strains in men (p < 0.05). Conclusions Women with DM demonstrated greater LV contractile dysfunction, which indicates that women with HFpEF comorbid with DM have a high-risk phenotype of cardiac failure that may require more aggressive and personalized medical treatment.

Background Weight management is strongly promoted for overweight or obese patients with type 2 diabetes (T2DM) by current guidelines. However, the prognostic impact of weight loss achieved without behavioural intervention on the mortality and cardiovascular (CV) outcomes in diabetic patients is still contested. Methods We searched the PubMed, Embase, and Cochrane Library databases for studies that investigated the association of weight loss or weight variability with mortality and CV outcomes. Results of studies that measured weight loss by percentage weight loss from baseline and stratified it as > 10% and 5–10% or studies that computed weight variability were pooled using random effects model. Study quality was evaluated using the Newcastle–Ottawa Scale. Results Thirty eligible studies were included in the systematic review and 13 of these were included in the meta-analysis. Large weight loss (> 10%) was associated with increased risk of all-cause mortality (pooled hazard ratio (HR) 2.27, 95% CI 1.51–3.42), composite of major CV events (pooled HR 1.71, 95% CI 1.38–2.12) and CV mortality (pooled HR 1.50, 95% CI 1.27–1.76) among T2DM patients. Moderate weight loss showed no significant association with all-cause mortality (pooled HR 1.17, 95% CI 0.97–1.41) or CV outcomes (pooled HR 1.12, 95% CI 0.94–1.33). Weight variability was associated with high hazard of all-cause mortality (pooled HR 1.54, 95% CI 1.52–1.56). Conclusions Large weight loss and large fluctuations in weight are potential markers of increased risk of mortality and CV events in T2DM patients. Maintaining a stable weight may have positive impact in these patients.

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