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Background: The data of the prognostic role of V‐Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations in early‐stage lung adenocarcinoma (LUAD) patients is scarce. This study aimed to investigate the proportion, clinicopathological features, and prognostic significance of patients with stage I LUAD carrying BRAF mutations. Methods: We collected 431 patients with pathological stage I LUAD from cBioPortal for Cancer Genomics and 1604 LUAD patients tested for BRAF V600E and epidermal growth factor receptor (EGFR) mutations from Shanghai Pulmonary Hospital. Survival curves were drawn by the Kaplan–Meier method and compared by log‐rank test. Cox proportional hazard models, propensity‐score matching (PSM), and overlap weighting (OW) were performed in this study. The primary endpoint was recurrence‐free survival (RFS). Results: The proportion of BRAF mutations was estimated at 5.6% in a Caucasian cohort. BRAF V600E mutations were detected in six (1.4%) patients in Caucasian populations and 16 (1.0%) patients in Chinese populations. Two BRAF V600E‐mutant patients were detected to have concurrent EGFR mutations, one for 19‐del and one for L858R. For pathological stage I LUAD patients, BRAF mutations were not significantly associated with worse RFS than wild‐type BRAF patients (HR = 1.111; p = 0.885). After PSM and OW, similar results were presented (HR = 1.352; p = 0.742 and HR = 1.246; p = 0.764, respectively). BRAF V600E mutation status also lacked predictive significance for RFS (HR, 1.844; p = 0.226; HR = 1.144; p = 0.831 and HR = 1.466; p = 0.450, respectively). Conclusions: In this study, we demonstrated that BRAF status may not be capable of predicting prognosis in stage I LUAD patients. There is a need for more data to validate our findings. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES Early-stage lung adenocarcinoma (ADC) has a great heterogeneity in prognosis that is difficult to evaluate effectively. Thus, we developed and validated an effective nomogram prognostic model based on the clinical and laboratory characteristics of stage I–IIA ADC. METHODS We included 1585 patients with pathologically diagnosed stage I–IIA ADC who underwent surgery at Shanghai Pulmonary Hospital. The nomogram was constructed based on the peripheral blood test and coagulation test indicators and evaluated using Calibration plots, concordance index, decision curve analysis and the X-tile software. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan–Meier method and the Cox proportional hazard regression model. The primary end point of this study was RFS. RESULTS Thrombin time and 4 clinical indicators for RFS were integrated into nomograms. A favourable agreement between the nomogram prediction and validation was observed in the calibration curves for RFS probabilities. The concordance index of the nomogram to predict RFS was 0.736 (95% confidence interval, 0.717–0.755). Moreover, significant differences were shown between the high-risk and low-risk groups in RFS and OS (P  <   0.001) after effective cut-off values of risk points were found based on the nomogram. CONCLUSIONS We established and validated a prognostic nomogram including thrombin time to predict RFS and OS of stage I–IIA ADC patients. This nomogram provided an effective prediction ability for the prognosis of stage I–IIA ADC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: A retrospective study was performed on 955 eligible patients with stage I lung adenocarcinoma (LUAD) after surgery. The systematic oxidative stress score (SOS) was established based on three biochemical indicators, including serum creatinine (CRE), lactate dehydrogenase (LDH), and uric acid (UA). SOS is an independent prognostic indicator for stage I LUAD. In addition, the constructed nomogram based on SOS could accurately predict the survival of those patients. This study aimed to construct an effective nomogram based on the clinical and oxidative stress-related characteristics to predict the prognosis of stage I lung adenocarcinoma (LUAD). A retrospective study was performed on 955 eligible patients with stage I LUAD after surgery at our hospital. The relationship between systematic-oxidative-stress biomarkers and the prognosis was analyzed. The systematic oxidative stress score (SOS) was established based on three biochemical indicators, including serum creatinine (CRE), lactate dehydrogenase (LDH), and uric acid (UA). SOS was an independent prognostic factor for stage I LUADs, and the nomogram based on SOS and clinical characteristics could accurately predict the prognosis of these patients. The nomogram had a high concordance index (C-index) (0.684, 95% CI, 0.656–0.712), and the calibration curves for recurrence-free survival (RFS) probabilities showed a strong agreement between the nomogram prediction and actual observation. Additionally, the patients were divided into two groups according to the cut-off value of risk points based on the nomogram, and a significant difference in RFS was observed between the high-risk and low-risk groups (p < 0.0001). SOS is an independent prognostic indicator for stage I LUAD. These things considered, the constructed nomogram based on SOS could accurately predict the survival of those patients. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the incidence and long-term survival outcomes of occult lung cancer between 2004 and 2015.Methods: A total of 2958 patients were diagnosed with occult lung cancer in the 305,054 patients with lung cancer. The entire cohort was used to calculate the crude incidence rate. Eligible 52,472 patients (T1-xN0M0, including 2353 occult lung cancers) were selected from the entire cohort to perform survival analyses after translating T classification according to the 8th TNM staging system. Cancer-specific survival curves for different T classifications were presented.Results: The crude incidence rate of occult lung cancer was 1.00 per 100 patients, and it was reduced between 2004 and 2015 [1.4 per 100 persons in 2004; 0.6 per 100 persons in 2015; adjusted risk ratio = 0.437, 95% confidence interval (CI) 0.363-0.527]. In the survival analysis, there were 2206 death events in the 2353 occult lung cancers. The results of the multivariable analysis revealed that the prognoses with occult lung cancer were similar to patients with stage T3N0M0 (adjusted hazard ratio = 1.054, 95% CI 0.986-1.127, p = 0.121). Adjusted survival curves presented the same results. In addition, adjusted for other confounders, female, age ≤ 72 years, surgical treatment, radiotherapy, adenocarcinoma, and non-squamous and non-adenocarcinoma non-small cell carcinoma were independent protective prognostic factors (all p < 0.05).Conclusions: Occult lung cancer was uncommon. However, the cancer-specific survival of occult lung cancer was poor, therefore, we should put the assessment of its prognoses on the agenda. Timely surgical treatment and radiotherapy could improve survival outcomes for those patients. Besides, we still need more research to confirm those findings. [ABSTRACT FROM AUTHOR]

Objectives: To construct a simplified prognostic risk model to predict overall survival after adjuvant radiotherapy for parotid gland carcinoma patients with stage T1-4aN1-3M0. Materials and Methods: We evaluated 879 patients who were pathological diagnosed as stage T1-4aN1-3M0 parotid gland cancer. Those eligible patients treated with parotidectomy and neck lymph node dissection between 2004 and 2015 in the Surveillance Epidemiology and End Results database. All cases received adjuvant radiotherapy. Independent prognostic factors included in the original model were identified by Cox regression analysis. The primary endpoint was overall survival. The model's prediction power was evaluated by the concordance index. The entire cohort was categorized into new low- and high-risk groups using X-tile software according to the results of prognostic model. Kaplan-Meier method was used to depict the survival curves. And the statistical significance was determined by log-rank test. Besides, a heat map was visually described the association between the survival time and 2 most significant prognostic factors. Results: In the univariable and multivariate analyses, 4 independent factors for overall survival were age, tumor size, pTNM stage, and the number of positive lymph nodes, which were all selected in the parsimonious prognostic model. The concordance indices of the prognostic model and pTNM stage were 0.652 and 0.565, respectively. Patients in the low-risk group had better overall survival over patients in the high-risk group [unadjusted hazard ratio = 2.578, 95% confidence interval 2.095-3.172, P < 0.001]. The results of the heat map revealed that patients with smaller tumor size and fewer positive lymph nodes had much longer survival time. Conclusions: This parsimonious prognostic model could estimate the long-term survival after adjuvant radiotherapy for parotid gland carcinoma with stage T1-4aN1-3N0M0. The tools may be valuable to guide multidisciplinary team in making treatment decisions. [ABSTRACT FROM AUTHOR]

Currently, the incidence of esophageal squamous cell carcinoma (ESCC) in China is high and its prognosis is poor. To evaluate the prognosis of patients with ESCC, we performed computerized quantitative analyses on diagnostic computed tomography (CT) and digital histopathological slices. A retrospective study was conducted to assess the prognosis of ESCC in 153 patients who underwent esophagectomy, and the cohort was selected based on strict clinical criteria. Each patient had an enhanced CT image, and there were two imaging protocols for CT images of all patients. Each patient in the cohort also had a histopathological tissue slide after hematoxylin–eosin staining. Under an electron microscope, the tissue slide was scanned as an image of large size. We then performed quantitative analyses to identify factors related to the prognosis of ESCC on digital histological images and diagnostic CT images. For CT images, we used the radiomics method. For histological images, we designed a set of quantitative features based on machine learning algorithms, such as K-means and principal component analysis. These features describe the patterns of different cell types in histopathological images. Subsequently, we used the survival analysis model established using only CT image features as the baseline. We also compared multiple machine learning models and adopted a five-fold cross-validation method to establish a robust survival model. In establishing survival models, we first used CT image features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.624. Then we used histopathlogical features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.664, which was obviously better than CT's. Lastly, we combined CT image features and histopathological image features to establish survival models. The performance was better than that in the models built using only CT image features or histopathological image features, and the C-index from the regularized Cox model on the test set reached 0.694. We also proved the effectiveness of the quantified histopathological image features in terms of prognosis using the log-rank test. Histopathological image features are more relevant to prognosis than features extracted from CT images using radiomics. The results of this study provide clinicians with a reference to improve the survival rate of patients with ESCC after surgery. These results have implications for advancing the process of explaining the poor prognosis of esophageal cancer. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of a novel computerized quantitative analysis based on histopathological and computed tomography (CT) images for predicting the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients. Methods: We retrospectively reviewed the medical records of 153 ESCC patients who underwent esophagectomy alone and quantitatively analyzed digital histological specimens and diagnostic CT images. We cut pathological images (6000 × 6000) into 50 × 50 patches; each patient had 14,400 patches. Cluster analysis was used to process these patches. We used the pathological clusters to all patches ratio (PCPR) of each case for pathological features and we obtained 20 PCPR quantitative features. Totally, 125 computerized quantitative (20 PCPR and 105 CT) features were extracted. We used a recursive feature elimination approach to select features. A Cox hazard model with L1 penalization was used for prognostic indexing. We compared the following prognostic models: Model A: clinical features; Model B: quantitative CT and clinical features; Model C: quantitative histopathological and clinical features; and Model D: combined information of clinical, CT, and histopathology. Indices of concordance (C-index) and leave-one-out cross-validation (LOOCV) were used to assess prognostic model accuracy. Results: Five PCPR and eight CT features were treated as significant indicators in ESCC prognosis. C-indices adjusted for LOOCV were comparable among four models, 0.596 (Model A) vs. 0.658 (Model B) vs. 0.651 (Model C), and improved to 0.711with Model D combining information of clinical, CT, and histopathology (all p<0.05). Using Model D, we stratified patients into low- and high-risk groups. The 3-year overall survival rates of low- and high-risk patients were 38.0% and 25.0%, respectively (p<0.001). Conclusion: Quantitative prognostic modeling using a combination of clinical data, histopathological, and CT images can stratify ESCC patients with surgery alone into high-risk and low-risk groups. [ABSTRACT FROM AUTHOR]