Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study.

BACKGROUND: Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC. METHODS: Clinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012-2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis. RESULTS: Eleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens. CONCLUSIONS: The decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website ( https://loyal-brand-611803.framer.app/ ).

Morphological, physiological, and structural responses of two species of artemisia to NaCl stress.

Effects of salt stress on Artemisia scoparia and A. vulgaris "Variegate" were examined. A. scoparia leaves became withered under NaCl treatment, whereas A. vulgaris "Variegate" leaves were not remarkably affected. Chlorophyll content decreased in both species, with a higher reduction in A. scoparia. Contents of proline, MDA, soluble carbohydrate, and Na(+) increased in both species under salt stress, but A. vulgaris "Variegate" had higher level of proline and soluble carbohydrate and lower level of MDA and Na(+). The ratios of K(+)/Na(+), Ca(2+)/Na(+), and Mg(2+)/Na(+) in A. vulgaris "Variegate" under NaCl stress were higher. Moreover, A. vulgaris "Variegate" had higher transport selectivity of K(+)/Na(+) from root to stem, stem to middle mature leaves, and upper newly developed leaves than A. scoparia under NaCl stress. A. vulgaris "Variegate" chloroplast maintained its morphological integrity under NaCl stress, whereas A. scoparia chloroplast lost integrity. The results indicated that A. scoparia is more sensitive to salt stress than A. vulgaris "Variegate." Salt tolerance is mainly related to the ability of regulating osmotic pressure through the accumulation of soluble carbohydrates and proline, and the gradient distribution of K(+) between roots and leaves was also contributed to osmotic pressure adjustment and improvement of plant salt tolerance.

Analysis of M6A associated lncRNAs in prognosis and immune response of NSCLC patients.

Distinguishing between N6-methyladenosine (m6A)-associated long noncoding RNAs (lncRNAs) is crucial in non-small-cell lung cancer (NSCLC) patients. In this research, the prognosis and immunotherapeutic response of lncRNAs and m6A in NSCLC were examined. lncRNAs related to m6A were identified using co-expression analyses, and their prognostic impact on patients with NSCLC was assessed using univariate Cox regression analysis. Sixty-three m6A-associated lncRNAs were determined as prognostic lncRNAs, and on this basis, 25 m6A-associated lncRNAs were screened by least absolute shrinkage and selection operator (lasso) Cox regression. Multivariable Cox analysis obtained 14 m6A-associated lncRNAs for the construction of risk model. The NSCLC patients were grouped into different risk subgroups in accordance with the median of the risk fraction in each data, and we evaluated the differences of potential immunotherapeutic characteristics and drug sensitivity prediction between the two subgroups. By using this model to recombine patients, they can be effectively distinguished in terms of the immunotherapy response. Furthermore, candidate compounds for the differentiation of NSCLC subtypes were identified. The model based on 14 m6A-associated lncRNAs is a promising prognostic biomarker, which may help to predict the efficacy of immunotherapy in NSCLC patients and provide a theoretical basis for improving the outcome of patients.

Combination strategies of immunotherapy in non-small cell lung cancer: facts and challenges.

ABSTRACT: Immunotherapy has dramatically altered the treatment of non-small cell lung cancer. Currently, the emergence of combination strategies in immunotherapy has brightened the prospects of improved clinical outcomes and manageable safety profiles in the first/second-line settings. However, sub-optimal response rates are still observed in several clinical trials. Hence, alternative combination models and candidate selection strategies need to be explored. Herein, we have critically reviewed and commented on the published data from several clinical trials, including combined immunotherapy and chemotherapy, anti-angiogenic agents, epidermal growth factor receptor/anaplastic lymphoma kinase tyrosine kinase inhibitors, radiotherapy, and other immune checkpoint inhibitors.

C-reactive protein to albumin ratio is a key indicator in a predictive model for anastomosis leakage after esophagectomy: Application of classification and regression tree analysis.

BACKGROUND: Anastomotic leakage (AL), a serious complication after esophagectomy, might impair patient quality of life, prolong hospital stay, and even lead to surgery-related death. The aim of this study was to show a novel decision model based on classification and regression tree (CART) analysis for the prediction of postoperative AL among patients who have undergone esophagectomy. METHODS: A total of 450 patients (training set: 356; test set: 94) with perioperative information were included. A decision tree model was established to identify the predictors of AL in the training set, which was validated in the test set. A receiver operating characteristic curve was also created to illustrate the diagnostic ability of the decision model. RESULTS: A total of 12.2% (55/450) of the 450 patients suffered AL, which was diagnosed at median postoperative day 7 (range: 6-16). The decision tree model, containing surgical duration, postoperative lymphocyte count, and postoperative C-reactive protein to albumin ratio, was established by CART analysis. Among the three variables, the postoperative C-reactive protein to albumin ratio was identified as the most important indicator in the CART model with normalized importance of 100%. According to the results validated in the test set, the sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of the prediction model were 80%, 98.8%, 88.9%, 97.6%, and 96.8%, respectively. Moreover, the area under the receiver operating characteristic curve was 0.95. CONCLUSION: The decision model based on CART analysis presented good performance for predicting AL, and might allow the early identification of patients at high risk.