OBJECTIVE: Mucinous breast cancer (MBC) is a kind of breast cancer (BC), which is rare in clinic, mainly for women, because of the low incidence rate, so there is no unified standard treatment protocol. Elderly patients have a poor prognosis due to their combined comorbidities. This study aims to investigate the effect of surgery and chemoradiotherapy on the prognosis of elderly female MBC patients and construct nomograms for predicting the OS and CSS in elderly female MBC patients.
METHODS: Data for female MBC patients over 65 years are obtained from the Surveillance, Epidemiology and End Results (SEER) database, patients were divided into two groups: the training set and the validation set. External validation data of the prediction model were provided by Kunming Hospital of Traditional Chinese Medicine. We used Cox regression modeling, which was used to identify independent risk factors affecting patient prognosis. After avoiding confounding bias according to the multifactorial Cox regression model, we used these screened statistically significant results to construct column-line plots. The performance of the model was tested using the consistency index (c-index), the calibration curve, and the area under the operating characteristic curve of the receiver (AUC). Subsequently, we used decision curve analysis (DCA) to examine the potential clinical value of our nomograms.
RESULTS: A total of 8103 elderly MBC female patients were extracted from the database SEER and were assigned to the training and validation set, randomly. A total of 83 patients from Kunming Hospital of Traditional Chinese Medicine were used in the external verification set. After multifactorial Cox regression analysis, we found that age, race, T-stage, M-stage, surgical approach, radiotherapy, and tumor size were independent risk factors for OS in elderly MBC patients. Similarly, independent risk factors of CSS included age, marital status, N stage, M stage, surgical approach, chemotherapy, and tumor size. The C-index for the OS training, validation, and external verification set were 0.731 (95%CI 0.715-0.747), 0.738 (95%CI 0.724-0.752), and 0.809 (95%CI 0.731-0.8874). The C-index of the training set, the validation set, and external verification set for CSS were 0.786 (95%CI 0.747-0.825), 0.776 (95%CI 0.737-0.815), and 0.84 (95%CI0.754-0.926), respectively. The AUC, calibration curves and DCA also showed good accuracy.
CONCLUSIONS: In this study, we construct a new nomogram to predict the prognosis of elderly patients with MBC. The nomograms have undergone internal and external validation and have been confirmed to have good clinical applicability. At the same time, we found that for elderly female MBC patients, surgery and radiotherapy significantly benefit their survival, but chemotherapy is not conducive to patient survival.

OBJECTIVE: Prostate cancer (PC) is a significant disease affecting men\'s health worldwide. More than 60% of patients over 65 years old and more than 80% are diagnosed with localized PC. The current choice of treatment modalities for localized PC and whether overtreatment is controversial. Therefore, we wanted to construct a nomogram to predict the risk factors associated with cancer-specific survival (CSS) and overall survival (OS) in elderly patients with localized PC while assessing the survival differences in surgery and radiotherapy for elderly patients with localized PC.
METHODS: Data of patients with localized PC over 65 years were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for CSS and OS. Nomograms predicting CSS and OS were built using multivariate Cox regression models. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the potential clinical value of this model.
RESULTS: A total of 90,434 patients over 65 years and diagnosed with localized PC from 2010 to 2018 were included in the study. All patients were randomly assigned to the training set (n = 63,328) and the validation set (n = 27,106). Univariate and multivariate Cox regression model analysis showed that age, race, marriage, T stage, surgical, radiotherapy, prostate-specific antigen (PSA), and Gleason score (GS) were independent risk factors for predicting CSS in elderly patients with localized PC. Age, race, marriage, surgery, radiotherapy, PSA, and GS were independent risk factors for predicting OS in elderly patients with localized PC. The c-index of the training and validation sets for the predicted CSS is 0.802(95%CI:0.788-0.816) and 0.798(95%CI:0.776-0.820, respectively). The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704-0.720) and 0.724(95%:0.714-0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability.
CONCLUSIONS: We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with reasonable accuracy, reliability and potential clinical value, the model can be used for clinically assisted decision-making.

Background and purpose: Sporadic cerebral amyloid angiopathy (CAA) is a small vessel disease, resulting from progressive amyloid-β deposition in the media/adventitia of cortical and leptomeningeal arterioles. We sought to assess the prevalence of baseline characteristics, clinical and radiological findings, as well as outcomes among patients with CAA, in the largest study to date conducted in Greece. Methods: Sixty-eight patients fulfilling the Boston Criteria v1.5 for probable/possible CAA were enrolled and followed for at least twelve months. Magnetic Resonance Imaging was used to assess specific neuroimaging markers. Data regarding cerebrospinal fluid biomarker profile and Apolipoprotein-E genotype were collected. Multiple logistic regression analyses were performed to identify predictors of clinical phenotypes. Cox-proportional hazard regression models were used to calculate associations with the risk of recurrent intracerebral hemorrhage (ICH). Results: Focal neurological deficits (75%), cognitive decline (57%), and transient focal neurological episodes (TFNEs; 21%) were the most common clinical manifestations. Hemorrhagic lesions, including lobar cerebral microbleeds (CMBs; 93%), cortical superficial siderosis (cSS; 48%), and lobar ICH (43%) were the most prevalent neuroimaging findings. cSS was independently associated with the likelihood of TFNEs at presentation (OR: 4.504, 95%CI:1.258-19.088), while multiple (>10) lobar CMBs were independently associated with cognitive decline at presentation (OR:5.418, 95%CI:1.316-28.497). cSS emerged as the only risk factor of recurrent ICH (HR:4.238, 95%CI:1.509-11.900) during a median follow-up of 20 months. Conclusions: cSS was independently associated with TFNEs at presentation and ICH recurrence at follow-up, while a higher burden of lobar CMBs with cognitive decline at baseline. These findings highlight the prognostic value of neuroimaging markers, which may influence clinical decision-making.

Biliary system cancers are most commonly gallbladder cancers (GBC). Elderly patients (≥ 65) were reported to suffer from an unfavorable prognosis. In this study, we analyzed the RNA-seq and clinical data of elderly GBC patients to derive the genetic characteristics and the survival-related nomograms.
RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.
Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.
Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.

OBJECTIVE: Metastatic early-onset colorectal cancer (EO-CRC) is on the rise, yet there is a dearth of predictive models for this disease. Therefore, it is crucial to develop a nomogram to aid in the early detection and management of metastatic colorectal cancer in young patients.
METHODS: We retrieved data from the SEER database on patients with metastatic colorectal cancer aged 50 or younger between 2010 and 2017. The data were randomly allocated in a 7:3 ratio to training and validation cohorts, and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) at 1, 3, and 5 years. The nomograms were developed based on these factors, and their discriminatory and calibration capabilities were validated. Using the nomogram risk scores, patients were stratified into low-risk and high-risk groups.
RESULTS: The study included 2470 patients with metastatic EO-CRC. Univariate and multivariate Cox regression analysis identified 12 independent risk factors that were included in the nomogram. The training cohort had a consistency index (C-index) of 0.71, while the validation cohort had a C-index of 0.70, demonstrating good predictive accuracy. Calibration plots showed a high level of consistency between the observed and predicted values, with overlapping plots along the diagonal. The decision curve analysis (DCA) revealed that the nomogram had a high clinical application value.
CONCLUSIONS: The novel nomograms were created to predict the prognosis of patients with metastatic EO-CRC, which can aid clinicians in developing more effective treatment strategies and contribute to more accurate prognostic assessments.

In order to identify the impact of local treatment on overall survival (OS) and cancer-specific survival(CSS) in men with mPCa.
Men with mPCa undergoing local treatment by radical prostatectomy (RP), radiotherapy (RT) including beam radiation and brachytherapy or no local treatment identified from Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). To evaluate local therapy impact on OS and CSS in relation to baseline characteristics, univariate and multivariable Cox regression analysis was used to predict the prognostic value of local therapy in OS and CSS.
A total of 902 (25.8%) patients received local treatment and 2598 (74.2%) patients did not receive local treatment in this study. The Kaplan-Meier curves showed that there was significant difference in OS between patients underwent local treatment and patients without local treatment (P = .013) but not in CSS (P = .068). While multivariate Cox regression analysis showed that local treatment may not significantly improve OS(P = .724). In subgroup analysis, Among patients with prostate-specific antigent (PSA)<10ng/ml, local treatment could significantly improve OS and CSS (all P < .05). Multivariate Cox regression analysis showed that local treatment could be used as an independent prognostic factor to improve OS in mPCa patients with PSA<10ng/ml (P = .031). Another multivariate Cox regression analysis demonstrated that patients with mPCa undergoing RP had better OS and CSS (all P < .05).
Our results showed that local salvage therapy did not seem to be an independent prognostic factor in all mPCa patients, but we found that local therapy can show a better prognosis in patients with lower PSA levels. Compared with RT, patients who had experienced RP may have better prognosis. We still need prospective research to further study the application value of local treatment in mPCa patients.

In order to high-value utilize the secondary solid waste calcium silicate slag (CSS) generated in the process of the extraction of alumina from fly ash, in this paper, tobermorite was synthesized using CSS and silica fume (SF) at different hydrothermal synthesis times. The hydrothermal synthesis was evaluated by means of XRD, SEM, EDS, and micropore analysis, and the results discussed. The results indicate that β-dicalcium silicate, the primary phase in the CSS, partially hydrates at the beginning of hydrothermal synthesis conditions to form mesh-like crystal C-S-H (calcium-rich) and calcium hydroxide. It then reacts with SF to form yarn-like crystal C-S-H (silicon-rich) and then furtherly grows into large flake-like crystal C-S-H (silicon-rich) at 3 h. When the synthesis time is 4 h, β-dicalcium silicate completely hydrates, and crystal C-S-H (calcium-rich) and calcium hydroxide further reacts with large flake-like crystal C-S-H (silicon-rich) to generate medium flake-like tobermorite. With the increase in time, the crystal of hydrothermal synthesis grows in the order of medium flake-like tobermorite, small flake-like tobermorite, strip flake-like tobermorite, fibrous-like tobermorite, and spindle-like tobermorite, and the APV, APD, and SSA show a trend of decreasing first, then increasing, and then decreasing. Meanwhile, strip flake-like tobermorite with a higher average pore volume (APV), average pore diameter (APD), and specific surface area (SSA) can be synthesized at 6 h.

Evaluation of chronic constipation is important, although it is often difficult to satisfactorily treat due to the complex interplay of factors. This study aimed to determine whether the volume of intraluminal contents and lateral diameter of the colon measured from computed tomography (CT) images were correlated with the symptoms of chronic constipation and stool consistency. Consecutive patients who underwent the Constipation Scoring System (CSS), Bristol Stool Form Scale (BSFS) questionnaires and simple abdominal CT were selected retrospectively. The intestinal tract diameter at each site was measured, and the amounts of stool and gas in the intestinal tract were evaluated at five levels. Of the 149 study participants, 54 were males and 95 were females and their mean age was 72.1 years. In the right hemi-colon, CSS5 (Time) correlated significantly with gas volume (p < 0.01). In the left hemi-colon, stool volume correlated significantly with CSS2 (Difficulty), CSS3 (Completeness), CSS5 (Time) and CSS total (p < 0.05). The BSFS negatively correlated with gas volume and diameter in the right hemi-colon and with gas volume in the rectum (p < 0.05). CT findings including stool volume, gas volume and diameter correlated with some constipation symptoms and stool consistency. These findings may be useful in evaluating and treating constipation.

Lung cancer is the leading cause of death from cancer and the number of operable elderly lung cancer patients is increasing, with advanced age being associated with a poorer prognosis. However, there is no easy and comprehensive prognostic assessment method for these patients.
Clinicopathological data of patients aged 65 years or older with TNM stage I-II lung cancer from 2004 to 2018 were downloaded from the SEER database. Patients from 2004 to 2015 were randomized into a training group (n = 16,457) and a validation group (n = 7,048). Data from 2016 to 2018 (n = 6,231) were used for external validation. Two nomogram prognostic models were created after independent prognostic factors connected to both overall survival (OS) and cancer-specific survival (CSS) in the training set by using univariate and multivariate Cox proportional hazards regression analysis. In turn, overall survival (OS) and cancer-specific survival (CSS) were predicted for patients at 1, 3, and 5 years. Based on the concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve (AUC), the time-dependent area under the ROC curve, the validity, accuracy, discrimination, predictive ability, and clinical utility of the models were evaluated. Decision curve analysis (DCA) was used to assess the clinical value of the models.
A total of 29,736 patients were included. Univariate and multivariate analyses suggested that age, race, gender, marriage, disease grade, AJCC stage, T-stage, surgery, radiotherapy, chemotherapy, and tumor size were independent risk factors for patient prognosis. These 11 variables were included in nomogram to predict OS and CSS of patients. C-indexes of OS for the training, validation and external validation sets were 0.730 (95% CI, 0.709-0.751), 0.734 (95% CI, 0.722-0.746), and 0.750 (95% CI, 0.734-0.766), respectively. The AUC results for the training and validation sets indicated good accuracy for this nomogram. The calibration curves demonstrated a high degree of concordance between actual and anticipated values, and the DCA demonstrated that the nomograms had better clinical application than the traditional TNM staging approach.
This study identified risk factors for survival in operable elderly lung cancer patients and established a new column line graph for predicting OS and CSS in these patients. The model has good clinical application and can be a good clinical decision-making tool for physicians and patients.

Prostate cancer (PC) is the second leading cause of cancer death in men in the United States after lung cancer in global incidence. Elderly male patients over 65 years old account for more than 60% of PC patients, and the impact of surgical treatment on the prognosis of PC patients is controversial. Moreover, there are currently no predictive models that can predict the prognosis of elderly PC patients undergoing surgical treatment. Therefore, we aimed to construct a new nomogram to predict cancer-specific survival (CSS) in elderly PC patients undergoing surgical treatment.
Data for surgically treated PC patients aged 65 years and older were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for elderly PC patients undergoing surgical treatment. A nomogram of elderly PC patients undergoing surgical treatment was developed based on the multivariate Cox regression model. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the predictive model. Decision curve analysis (DCA) was used to examine the potential clinical value of this model.
A total of 44,975 elderly PC patients undergoing surgery in 2010-2018 were randomly assigned to the training set (N = 31705) and validation set (N = 13270). the training set was used for nomogram development and the validation set was used for internal validation. Univariate and multivariate Cox regression model analysis showed that age, marriage, TNM stage, surgical style, chemotherapy, radiotherapy, Gleason score(GS), and prostate-specific antigen(PSA) were independent risk factors for CSS in elderly PC patients undergoing surgical treatment. The C index of the training set and validation indices are 0.911(95%CI: 0.899-0.923) and 0.913(95%CI: 0.893-0.933), respectively, indicating that the nomogram has a good discrimination ability. The AUC and the calibration curves also show good accuracy and discriminability.
To our knowledge, our nomogram is the first predictive model for elderly PC patients undergoing surgical treatment, filling the gap in current predictive models for this PC patient population. Our data comes from the SEER database, which is trustworthy and reliable. Moreover, our model has been internally validated in the validation set using the C-index,AUC and the and the calibration curve, showed that the model have good accuracy and reliability, which can help clinicians and patients make better clinical decision-making. Moreover, the DCA results show that our nomogram has a better potential clinical application value than the TNM staging system.