Predictor Of Cancer-specific Survival Research Articles

Individualized prediction of conditional survival for colorectal signet-ring cell carcinoma patients.

Conditional survival (CS) considers the time already survived after surgery and may provide additional survival information. The authors sought to construct and validate novel conditional survival nomograms for the prediction of conditional overall survival (OS) and cancer-specific survival (CSS) of colorectal signet-ring cell carcinoma (SRCC) patients. Patients diagnosed with stage I-III SRCC between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The formula calculating CS was: CS(x|y) = S(x+y)/S(x), where S(x) represents the survival at x years. CS nomograms were then constructed to predict the 5-year conditional OS and CSS, followed by internal validation. A total of 944 colorectal SRCC patients were finally identified in this study. The 5-year OS and CSS improved gradually with additional survival time. Univariate and multivariate Cox regression analysis conducted in training set revealed that age, race, T stage, LNR, and perineural invasion were independent risk factors for both OS and CSS. Two nomograms with considerable predictive ability were successfully constructed [area under the curve (AUC) for OS: 0.788; AUC for CSS: 0.847] and validated (AUC for OS: 0.773; AUC for CSS: 0.799) for the prediction of 5-year OS and CSS, based on the duration of 1-4 years post-surgery survival. The probability of achieving 5-year OS and 5-year CSS in colorectal SRCC patients improved gradually with additional time. Conditional nomograms considering survival time will be more reliable and informative for risk stratification and postoperative follow-up.

Surgically treated pelvic liposarcoma and leiomyosarcoma: The effect of tumor size on cancer-specific survival

IntroductionIn soft tissue pelvic liposarcoma and leiomyosarcoma, it is unknown whether a specific tumor size cut-off may help to better predict prognosis, defined as cancer-specific survival (CSS). We tested whether different tumor size cut-offs, could improve CSS prediction. Materials and methodsSurgically treated non-metastatic soft tissue pelvic sarcoma patients were identified (Surveillance, Epidemiology, and End Results 2004–2019). Kaplan-Meier plots, univariable and multivariable Cox-regression models and receiver operating characteristic-derived area under the curve (AUC) estimates were used. ResultsOverall, 672 (65 %) liposarcoma (median tumor size 11 cm, interquartile range [IQR] 7–16) and 367 (35 %) leiomyosarcoma (median tumor size 8 cm, IQR 5–12) patients were identified. The p-value derived ideal tumor size cut-off was 17.1 cm, in liposarcoma and 7.0 cm, in leiomyosarcoma. In liposarcoma, according to p-value derived cut-off, five-year CSS rates were 92 vs 83 % (≤17.1 vs > 17.1 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 83.8 to 86.8 % (Δ = 3 %). Similarly, among previously established cut-offs (5 vs 10 vs 15 cm), also 15 cm represented an independent predictor of CSS and improved prognostic ability from 83.8 to 87.0 % (Δ = 3.2 %). In leiomyosarcoma, according to p-value derived cut-off, five-year CSS rates were 86 vs 55 % (≤7.0 vs > 7.0 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 68.6 to 76.5 % (Δ = 7.9 %). ConclusionsIn liposarcoma, the p-value derived tumor size cut-off was 17.1 cm vs 7.0 cm, in leiomyosarcoma. In both histologic subtypes, these cut-offs exhibited the optimal statistical characteristics (univariable, multivariable and AUC analyses). In liposarcoma, the 15 cm cut-off represented a valuable alternative.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database.

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma. Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms. We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification. The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

Identifying optimal surgical approach among T1N2-3M0 non-small cell lung cancer patients: a population-based analysis.

Whether stage T1N2-3M0 non-small cell lung cancer (NSCLC) patients could benefit from surgery and the optimal surgical procedure have remained controversial and unclear. This study aimed to investigate whether stage T1N2-3M0 NSCLC can benefit from different surgery types and develop a tool for survival prediction. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients diagnosed with stage T1N2-3M0 NSCLC between 2000 and 2015. A 1:1 propensity score-matched (PSM) analysis was used to balance the distribution of clinical characteristics. Survival analyses were performed by using the Kaplan-Meier (KM) curves and Cox proportional hazards regression. All patients were randomly split at a ratio of 7:3 into training and validation cohorts. The nomogram was constructed by integrating all independent predictors for overall survival (OS) and cancer-specific survival (CSS). The model's performance was evaluated by discrimination, calibration ability, and risk stratification ability. A total of 4,671 patients were enrolled. After 1:1 PSM, the distribution proportions of clinical characteristics in 1,146 patients were balanced (all P>0.05). The non-surgical approach was associated with worse survival compared with sublobectomy and lobectomy in the unmatched and matched cohorts. The multivariate Cox analysis showed that sublobectomy and lobectomy were both related to better OS and CSS rates compared with no surgery (P<0.001). Moreover, the results of subgroup analyses based on age, N stage, and radiotherapy or chemotherapy strategy were consistent. A total of 801 patients were included in the training cohort and 345 cases constituted the validation cohort. The nomogram constructed for the 1-, 3-, and 5-year OS and CSS prediction showed good discrimination, performance, and calibration both in the training and validation sets. Significant distinctions in survival curves between different risk groups stratified by prognostic scores were also observed (all P<0.001). Stage T1N2-3M0 NSCLC patients could benefit from sublobectomy or lobectomy, and lobectomy provides better survival benefits. We developed and validated nomograms, which could offer clinicians instructions for strategy making.

Construction and validation of log odds of positive lymph nodes (LODDS)-based nomograms for predicting overall survival and cancer-specific survival in ovarian clear cell carcinoma patients.

Ovarian clear cell carcinoma (OCCC) is one of the special histologic subtypes of ovarian cancer. This study aimed to construct and validate log odds of positive lymph nodes (LODDS)-based nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Patients who underwent surgical treatment between 2010 and 2016 were extracted from the Surveillance Epidemiology and End Results (SEER) database and the data of OCCC patients from the First Affiliated Hospital of Dalian Medical University were used as the external validation group to test the validity of the prognostic model. The best-fitting models were selected by stepwise Cox regression analysis. Survival probability was calculated by the Kaplan-Meier method, and the differences in survival time between subgroups were compared using the log-rank test. Each nomogram's performance was assessed by the calibration plots, decision curve analysis (DCA), and receiver operating characteristics (ROC) curves. T stage, distant metastasis, marital status, and LODDS were identified as significant risk factors for OS. A model with four risk factors (age, T stage, stage, and LODDS value) was obtained for CSS. Nomograms were constructed by incorporating the prognostic factors to predict 1-, 3- and 5-year OS and CSS for OCCC patients, respectively. The area under the curve (AUC) range of our nomogram model for OS and CSS prediction ranged from 0.738-0.771 and 0.769-0.794, respectively, in the training cohort. The performance of this model was verified in the internal and external validation cohorts. Calibration plots illustrated nomograms have good prognostic reliability. Predictive nomograms were constructed and validated to evaluate the OS and CSS of OCCC patients. These nomograms may provide valuable prognostic information and guide postoperative personalized care in OCCC.

Serum α-hydroxybutyrate dehydrogenase as a biomarker for predicting survival outcomes in patients with UTUC after radical nephroureterectomy.

We aimed to determine the prognostic value of α-hydroxybutyrate dehydrogenase (α-HBDH) in upper tract urothelial carcinoma (UTUC) patients after radical nephroureterectomy (RNU). We retrospectively enrolled the data of 544 UTUC patients at West China Hospital from May 2003 to June 2019. Cancer-specific survival (CSS) was the endpoint of interest. The optimal cutoff value of α-HBDH was identified by X-Tile program. After propensity score matching (PSM), we utilized Kaplan‒Meier curves to estimate survival and Cox proportional hazard model for risk assessment. A nomogram was built based on the results of multivariate analysis, and calibration curve, time-dependent receiver operating characteristic (ROC) curves and decision curve analysis were also performed to evaluate the predictive accuracy. Overall, 394 and 150 patients were divided into the α-HBDH-low group and α-HBDH -high group at the cutoff value of 158 U/L, respectively. After PSM, the two groups were well matched for all confounding factors. High α-HBDH was associated with inferior CSS (P = 0.006), and preoperative α-HBDH was an independent predictor for CSS (HR: 1.36; 95% CI:1.08, 1.80), especially in localized UTUC patients (HR: 2.04; 95% CI:1.11, 3.74). Furthermore, the nomogram based on α-HBDH achieved great predictive ability for CSS with areas under the curves of 0.800 and 0.778 for 3-year and 5-year CSS, respectively. Serum α-HBDH was a novel and reliable biomarker for predicting survival outcomes in UTUC patients after RNU but should be further explored.

The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study

Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.

Prognostic value of tumor deposits and positive lymph node ratio in stage III colorectal cancer: a retrospective cohort study.

In colorectal cancer (CRC), tumor deposits (TD) have been used to guide the N staging only in node-negative patients. It remains unknown about the prognostic value of TD in combination with positive lymph node ratio (LNR) in stage III CRC. We analyzed data from 31,139 eligible patients diagnosed with stage III CRC, including 30,230 from the Surveillance, Epidemiology, and End Results (SEER) database as a training set and 909 from two Chinese hospitals as a validation set. The associations of TD and LNR with cancer-specific survival (CSS) and overall survival (OS) were evaluated using the Kaplan-Meier method and Cox regression models. Both TD-positive and high LNR (value≥0.4) were associated with worse CSS in the training (multivariable hazard ratio [HR], 1.50; 95% confidence interval [CI], 1.43-1.58 and HR, 1.74; 95% CI, 1.62-1.86, respectively) and validation sets (HR,1.90; 95%CI, 1.41-2.54 and HR,2.01; 95%CI, 1.29-3.15, respectively). Compared to patients with TD-negative and low LNR (value<0.4), those with TD-positive and high LNR had a 4.09-fold risk of CRC-specific death in the training set (HR, 4.09; 95% CI, 3.54-4.72) and 4.60-fold risk in the validation set (HR, 4.60; 95% CI, 2.88-7.35). Patients with TD-positive/H-LNR CRC on the right side had the worst prognosis (P<0.001). The combined variable of TD and LNR contributed the most to CSS prediction in the training (24.26%) and validation (32.31%) sets. A nomogram including TD and LNR showed satisfactory discriminative ability, and calibration curves indicated favorable consistency in both the training and validation sets. TD and LNR represent independent prognostic predictors for stage III CRC. A combination of TD and LNR could be used to identify those at high risk of CRC deaths.

Application of Survival Quilts for prognosis prediction of gastrectomy patients based on the Surveillance, Epidemiology, and End Results database and China National Cancer Center Gastric Cancer database

ObjectiveAccurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients. We aimed to develop and test 6-month, 1-, 2-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) prediction models for gastric cancer patients following gastrectomy. MethodsWe derived and tested Survival Quilts, a machine learning-based model, to develop 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS prediction models. Gastrectomy patients in the development set (n = 20,583) and the internal validation set (n = 5,106) were recruited from the Surveillance, Epidemiology, and End Results (SEER) database, while those in the external validation set (n = 6,352) were recruited from the China National Cancer Center Gastric Cancer (NCCGC) database. Furthermore, we selected gastrectomy patients without neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis (TNM) stage. Prognostic performances of these OS and CSS models were assessed using the Concordance Index (C-index) and area under the curve (AUC) values. ResultsThe machine learning model had a consistently high accuracy in predicting 6-month, 1-, 2-, 3-, 5-, and 10-year OS in the SEER development set (C-index = 0.861, 0.832, 0.789, 0.766, 0.740, and 0.709; AUC = 0.784, 0.828, 0.840, 0.849, 0.869, and 0.902, respectively), SEER validation set (C-index = 0.782, 0.739, 0.712, 0.698, 0.681, and 0.660; AUC = 0.751, 0.772, 0.767, 0.762, 0.766, and 0.787, respectively), and NCCGC set (C-index = 0.691, 0.756, 0.751, 0.737, 0.722, and 0.701; AUC = 0.769, 0.788, 0.790, 0.790, 0.787, and 0.788, respectively). The model was able to predict 6-month, 1-, 2-, 3-, 5-, and 10-year CSS in the SEER development set (C-index = 0.879, 0.858, 0.820, 0.802, 0.784, and 0.774; AUC = 0.756, 0.827, 0.852, 0.863, 0.874, and 0.884, respectively) and SEER validation set (C-index = 0.790, 0.763, 0.741, 0.729, 0.718, and 0.708; AUC = 0.706, 0.758, 0.767, 0.766, 0.766, and 0.764, respectively). In multivariate analysis, the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set (hazard ratio [HR] = 14.59, 95% confidence interval [CI]: 1.872–2.774, P < 0.001), SEER validation set (HR = 2.28, 95% CI: 13.089–16.293, P < 0.001), and NCCGC set (HR = 1.98, 95% CI: 1.617–2.437, P< 0.001). We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients, and found that high-risk group remained as an independent CSS factor in the SEER development set (HR = 12.81, 95% CI: 11.568–14.194, P < 0.001) and SEER validation set (HR = 1.61, 95% CI: 1.338–1.935, P < 0.001). ConclusionSurvival Quilts could allow accurate prediction of 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS in gastric cancer patients following gastrectomy.

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient’s prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan–Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

Adrenalectomy for Metastasis: The Impact of Primary Histology on Survival Outcome.

Adrenalectomy is commonly considered a curative treatment in case of adrenal gland as site of metastasis. In the present study, we evaluated the impact of primary tumor histology on survival outcomes after a minimally invasive adrenal mastectomy for a solitary metachronous metastasis. From May 2004 to August 2020, we prospectively collected data on minimally invasive adrenalectomies whose pathological examination showed a metastasis. All patients only received metastasectomies that were performed with curative intent, or to achieve non-evidence of disease status. Adjuvant systemic therapy was not administered in any case. Cancer-specific survival (CSS) was assessed using the Kaplan-Meier method. Univariable and multivariable Cox regression analyses were applied to identify independent predictors of CSS. Out of 235 laparoscopic and robotic adrenalectomies, the pathologic report showed metastases in 60 cases. The primary histologies included 36 (60%) renal cell carcinoma (RCC), 9 (15%) lung cancer, 6 (10%) colon cancer, 4 (6.7%) sarcoma, 3 (5%) melanoma and 2 (3.3%) bladder cancer. RCC displayed significantly longer survival rates with a 5-year CSS of 55.9%, versus 22.8% for other histologies (log-rank p = 0.01). At univariable analysis, disease-free interval (defined as the time from adrenalectomy to evidence of disease progression) < 12 months and histology were predictors of CSS (p = 0.003 and p < 0.001, respectively). At multivariable Cox analysis, the only independent predictor of CSS was primary tumor histology (p = 0.005); patients with adrenal metastasis from colon cancer and bladder cancer showed a 5.3- and 75.5-fold increased risk of cancer death, respectively, compared to patients who had RCC as primary tumor histology. Oncological outcomes of adrenal metastasectomies are strongly influenced by primary tumor histology. A proper discussion of the role of surgery in a multidisciplinary context could provide optimal treatment strategies.

A novel nomogram and recursive partitioning analysis for predicting cancer-specific survival of patients with subcutaneous leiomyosarcoma

Accurately predicting prognosis subcutaneous leiomyosarcoma (LMS) is crucial for guiding treatment decisions in patients. The objective of this study was to develop prediction models for cancer-specific survival (CSS) in patients with subcutaneous LMS. The collected cases of diagnosed subcutaneous LMS were randomly divided into a training cohort and a validation cohort at a 6:4 ratio based on tumor location and histological code. The X-tile program was utilized to determine the optimal cutoff points for age index. Univariate and Cox multivariate regression analyses were conducted to identify independent risk factors for subcutaneous LMS patients. Nomograms were constructed to predict CSS, and their performance was assessed using C-index and calibration plots. Additionally, a decision tree model was established using recursive partitioning analysis to determine the total score for CSS prediction in subcutaneous LMS patients based on the nomogram model. A total of 1793 patients with subcutaneous LMS were found. X-tile software divides all patients into ≤ 61 years old, 61–82 years old, and ≥ 82 years old. The most important anatomical sites were the limbs (including the upper and lower limbs, 48.0%). Only 6.2% of patients received chemotherapy, while 44% had a history of radiotherapy and 92.9% underwent surgery. The independent risk factors for patients with subcutaneous LMS were age, summary stage, grade, and surgery. CSS was significantly decreased in patients with distant metastases, which showed the highest independent risk predictor (HR 4.325, 95% CI 3.010–6.214, p < 0.001). The nomogram prediction model of LMS was constructed based on four risk factors. The C-index for this model was 0.802 [95% CI 0.781–0.823] and 0.798 [95% CI 0.768–0.829]. The training cohort's 3-, 5-, and 10-year AUCs for CSS in patients with subcutaneous LMS were 0.833, 0.830, and 0.859, and the validation cohort's AUC for predicting CSS rate were 0.849, 0.830, and 0.803, respectively. Recursive segmentation analysis divided patients into 4 risk subgroups according to the total score in the nomogram, including low-risk group < 145, intermediate-low-risk group ≥ 145 < 176, intermediate-high-risk group ≥ 176 < 196, and high-risk group ≥ 196; The probability of the four risk subgroups is 9.1%, 34%, 49%, and 79% respectively. In this retrospective study, a novel nomogram or corresponding risk classification system for patients with subcutaneous LMS were developed, which may assist clinicians in identifying high-risk patients and in guiding the clinical decision.

Multivariable model versus AJCC staging system: cancer-specific survival predictions in adrenocortical carcinoma.

We developed a novel contemporary population-based model for predicting cancer-specific survival (CSS) in adrenocortical carcinoma (ACC) patients and compared it with the established 8th edition of the American Joint Committee on Cancer staging system (AJCC). Within the Surveillance, Epidemiology, and End Results database (2004-2020), we identified 1056 ACC patients. Univariable Cox regression model addressed CSS. Harrell's concordance index (C-index) quantified accuracy after 2000 bootstrap resamples for internal validation. The multivariable Cox regression model included the most informative, statistically significant predictors. Calibration and decision curve analyses (DCAs) tested the multivariable model as well as AJCC in head-to-head comparisons. Age at diagnosis (>60 vs ≤60 years), surgery, T, N, and M stages were included in the multivariable model. Multivariable model C-index for 3-year CSS prediction was 0.795 vs 0.757 for AJCC. Multivariable model outperformed AJCC in DCAs for the majority of possible CSS-predicted values. Both models exhibited similar calibration properties. Finally, the range of the multivariable model CSS predicted probabilities raged 0.02-75.3% versus only four single AJCC values, specifically 73.2% for stage I, 69.7% for stage II, 46.6% for stage III, and 15.5% for stage IV. The greatest benefit of the multivariable model-generated CSS probabilities applied to AJCC stage I and II patients. The multivariable model was more accurate than AJCC staging when CSS predictions represented the endpoint. Additionally, the multivariable model outperformed AJCC in DCAs. Finally, the AJCC appeared to lag behind the multivariable model when discrimination addressed AJCC stage I and II patients.

Nomogram for predicting the overall survival and cancer-specific survival of patients with intraductal carcinoma of the prostate

PurposeIntraductal carcinoma of the prostate (IDC-P) is a histological subtype that differs from conventional acinar adenocarcinoma in terms of its origin, appearance, and pathological features. For IDC-P, there is currently no recognized best course of action, and its prognosis is unclear. The goal of this study is to analyze independent prognostic factors in IDC-P patients and to develop and validate a nomogram to predict overall survival (OS) and cancer-specific survival (CSS).MethodsClinical data for IDC-P patients were collected from the Surveillance, Epidemiology, and End Results database. To identify the independent variables influencing prognosis, multivariate Cox regression analysis was performed. A nomogram model was created utilizing these variables after comparing the variations in OS and CSS among various subgroups using Kaplan‒Meier curves. Internal validation of the nomograms was verified using the bootstrap resampling method.ResultsThe study included 280 IDC-P patients in total. Marital status, summary stage, grade, and the presence of lung metastases were significant factors impacting OS, and CSS was significantly influenced by marital status, summary stage, AJCC stage, the presence of lung metastases, the presence of bone metastases, and PSA according to univariate and multivariate Cox regression models (P < 0.05). Nomogram models were created to estimate OS and CSS using these parameters. The OS prediction model’s C-index was 0.744, whereas the CSS prediction model’s C-index was 0.831.ConclusionWe developed and verified nomogram models for the prediction of 1-, 3-, and 5-year OS and CSS in patients with IDC-P. These nomograms serve as a resource for evaluating patient prognosis, therapy, and diagnosis, ultimately improving clinical decision-making accuracy.

Development and validation of web-based nomograms for predicting survival status in patients with intrahepatic cholangiocarcinoma depending on the surgical status: a SEER database analysis

This study aimed to develop and validate prognostic nomograms that can estimate the probability of 1-, 3- and 5-year overall survival (OS) as well as cancer-specific survival (CSS) for Intrahepatic cholangiocarcinoma (ICCA) patients. Clinical data of 1446 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. In both the OS and the CSS group, the training cohort and validation cohort were divided into a 7:3 ratio. Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgical status, and tumor grade were selected as independent prognostic risk factors to build the nomograms. To compare the efficacy of predicting 1-, 3-, and 5-year OS and CSS rates of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. The results showed the nomogram for 1-, 3-, and 5-year OS and CSS prediction performed better than the AJCC staging system. In the subgroup analysis for patients could not receive surgery as the primary treatment. We developed two nomograms for predicting the 1-, and 2-year OS and CSS rates following the same analysis procedure. Results indicate that the performance of both nomograms, which contained sex, AJCC T stage, AJCC M stage, chemotherapy, and tumor grade and prognostic factors, was also superior to the AJCC staging system. Meanwhile, four dynamic network-based nomograms were published. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (P < 0.0001). Finally, accurate and convenient nomograms were established to assist clinicians in making more personalized prognosis predictions for ICCA patients.

Competing risk nomogram and risk classification system for evaluating overall and cancer-specific survival in neuroendocrine carcinoma of the cervix: a population-based retrospective study.

Neuroendocrine carcinoma of the cervix (NECC) is a rare malignancy with poor clinical prognosis due to limited therapeutic options. This study aimed to establish a risk-stratification score and nomogram models to predict prognosis in NECC patients. Data on individuals diagnosed with NECC between 2000 and 2019 were retrieved from the Surveillance Epidemiology and End Results (SEER) database and then randomly classified into training and validation cohorts (7:3). Univariate and multivariate Cox regression analyses evaluated independent indicators of prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis further assisted in confirming candidate variables. Based on these factors, cancer-specific survival (CSS) and overall survival (OS) nomograms that predict survival over 1, 3, and 5years were constructed. The receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve estimated the precision and discriminability of the competing risk nomogram for both cohorts. Finally, we assessed the clinical value of the nomograms using decision curve analysis (DCA). Data from 2348 patients were obtained from the SEER database. Age, tumor stage, T stage, N stage, chemotherapy, radiotherapy, and surgery predicted OS. Additionally, histological type was another standalone indicator of CSS prognosis. For predicting CSS, the C-index was 0.751 (95% CI 0.731 ~ 0.770) and 0.740 (95% CI 0.710 ~ 0.770) for the training and validation cohorts, respectively. Furthermore, the C-index in OS prediction was 0.757 (95% CI 0.738 ~ 0.776) and 0.747 (95% CI 0.718 ~ 0.776) for both cohorts. The proposed model had an excellent discriminative ability. Good accuracy and discriminability were also demonstrated using the AUC and calibration curves. Additionally, DCA demonstrated the high clinical potential of the nomograms for CSS and OS prediction. We constructed a corresponding risk classification system using nomogram scores. For the whole cohort, the median CSS times for the low-, moderate-, and high-risk groups were 59.3, 19.5, and 7.4months, respectively. New competing risk nomograms and a risk classification system were successfully developed to predict the 1-, 3-, and 5-year CSS and OS of NECC patients. The models are internally accurate and reliable and may guide clinicians toward better clinical decisions and the development of personalized treatment plans.

Prognostic values of blood urea nitrogen/creatinine and cystatin C in patients with radical nephrectomy for renal cell carcinoma

Background: To evaluate the prognostic value of blood urea nitrogen/creatinine ratio (BUN/SCr) and cystatin C (Cys C) in patients with renal cell carcinoma (RCC) after radical nephrectomy.&#x0D; Methods: The study analyzed 348 patients with RCC who underwent radical nephrectomy. The optimal cutoff was obtained based on the ROC of specific survival outcomes and the maximum Youden index, and the patients were divided into four groups: Group 1 (low BUN/SCr-low Cys C), Group 2 (low BUN/SCr-high Cys C), Group 3 (high BUN/SCr-low Cys C) and Group 4 (high BUN/SCr-high Cys C). The primary endpoint was cancer-specific survival (CSS) and the secondary endpoint was disease-free survival (DFS).&#x0D; Results: A strong positive correlation was shown between BUN/SCr value and Cys C level. Patients with a higher BUN/SCr ratio (17.41) and Cys C level (3.98 mg/L) had poorer survival outcomes. Notably, patients in group 4 showed the worst CSS and DFS rates, while patients in groups 1 and 2 had better survival outcomes with no significant difference between the two groups. A higher BUN/SCr ratio (17.41) and high Cys C serum level (3.98 mg/L) were independent predictors of CSS and DFS, in addition to preoperative tumor size and pathological T (pT) stage.&#x0D; Conclusion: This study provides the first evidence of the independent prognostic importance of BUN/SCr ratio and Cys C in patients with RCC after radical nephrectomy.

Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma：A population-based retrospective study

BackgroundPenile cancer is a rare tumor and few studies have focused on the prognosis of M0 penile squamous cell carcinoma (PSCC). This retrospective study aimed to identify independent prognostic factors and construct predictive models for the overall survival (OS) and cancer-specific survival (CSS) of patients with M0 PSCC. MethodsData was extracted from the Surveillance, Epidemiology, and End Results database for patients diagnosed with malignant penile cancer. Eligible patients with M0 PSCC were selected according to predetermined inclusion and exclusion criteria. These patients were then divided into a training set, a validation set, and a test set. Univariate and multivariate COX regression analyses were initially performed to identify independent prognostic factors for OS and CSS in M0 PSCC patients. Subsequently, traditional and machine learning prognostic models, including random survival forest (RSF), COX, gradient boosting, and component-wise gradient boosting modelling, were constructed using the scikit-survival framework. The performance of each model was assessed by calculating time-dependent area under curve (AUC), C-index, and integrated Brier score (IBS), ultimately identifying the model with the highest performance. Finally, the Shapley additive explanation (SHAP) value, feature importance, and cumulative rates analyses were used to further estimate the selected model. ResultsA total of 2, 446 patients were included in our study. Cox regression analyses demonstrated that age, N stage, and tumor size were predictors of OS, while the N stage, tumor size, surgery, and residential area were predictors of CSS. The RSF and COX models had a higher time-independent AUC and C-index, and lower IBS value than other models in OS and CSS prediction. Feature importance analysis revealed the N stage as a common significant feature for predicting M0 PSCC patients’ survival. The SHAP and cumulative rate analyses demonstrated that the selected models can effectively evaluate the prognosis of M0 PSCC patients. ConclusionIn M0 PSCC patients, age, N stage, and tumor size were predictors of OS. In addition, the N stage, tumor size, surgery, and residential area were predictors of CSS. The machine learning-based RSF and COX models effectively predicted the prognosis of M0 PSCC patients.

Clinicopathological Features and Survival Trends of Non-Epithelial Ovarian Cancer: Analysis of the Surveillance, Epidemiology, and End Results (SEER) Database

Introduction: Owing to their low incidence, no reliable statistics about prognostication derived from large sample sizes have been reported of malignant ovarian germ cell tumors (MOGCTs) and sex cord-stromal tumors (SCSTs). The present study aimed to investigate the clinicopathological prognostic factors and the survival trends of MOGCTs and SCSTs. Materials and Methods: Patients with MOGCTs and SCSTs were recorded in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed between 2000 and 2019. Clinical, demographic, and treatment characteristics were compared between groups of MOGCTs and SCSTs. Cox risk regression analysis and Kaplan-Meier survival curves were used to compare overall survival (OS) and cancer-specific survival (CSS) and to assess the prognostic factors. Results: Information about 2,506 patients with MOGCTs and 1,556 patients with SCSTs was extracted from the SEER database, respectively. Aged <40 years and single were more common in patients with MOGCTs than in those with SCSTs. The vast majority of patients with MOGCTs and SCSTs underwent surgery (98.1% vs. 94.5%; p < 0.001), and women with MOGCTs were more likely to receive chemotherapy than women with SCSTs (56.1% vs. 32.2%; p < 0.001). For both patients before and after propensity-score matching, the 5-year OS rates of patients with SCSTs were lower than those of patients with MOGCTs (p < 0.05). In multivariate Cox regression analysis, both age and surgery were independent predictors of OS in patients with MOGCTs and SCSTs. FIGO staging was an independent predictor of CSS in MOGCT patients. Tumor size and chemotherapy were also independent predictors of CSS in patients with SCSTs. Conclusion: Compared to patients with SCSTs, those with MOGCTs tended to be younger and had a higher OS and CSS. Adjuvant chemotherapy after surgery did not prolong OS and CSS in patients with SCSTs.

Personalized prognostic prediction tool for high-grade neuroendocrine cervical cancer: a SEER database analysis and single-center validation

PurposeCervical high-grade neuroendocrine carcinoma (CHGNEC) is a rare but highly aggressive cancer. The purpose of this study is to develop a prognostic nomogram that can accurately predict the outcomes for CHGNEC patients.MethodsWe analyzed clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of CHGNEC patients, including small-cell neuroendocrine carcinoma (SCNEC) and large-cell neuroendocrine carcinoma (LCNEC). We investigated patient characteristics and prognosis, and developed a prognostic nomogram model for cancer-specific survival in CHGNEC patients. External validation was conducted using real clinical cases from our hospital.ResultsOur study included 306 patients from SEER database, with a mean age of 49.9 ± 15.5 years. Most of the patients had SCNEC (86.9%). Among them, 170 died from the disease, while 136 either survived or died from other causes. Our final predictive model identified age at diagnosis, stage 1 status, stage 4 status, T1, N0, and surgery of the primary site as independent prognostic factors for CHGNEC. We validated our model using a group of 16 CHGNEC patients who underwent surgery at our center. The external validation showed that the prognostic nomogram had excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.49–1.00) for the prediction of 3-year cancer-specific survival (CSS) and an AUC of 0.85 (95% CI 0.62–1.00) for the prediction of 5-years CSS. The random survival forest model achieved an AUC of 0.80 (95% CI 0.56–1.00) for 3-years CSS and 0.91 (95% CI 0.72–1.00) for 5-years CSS, indicating its adequacy in predicting outcomes for CHGNEC patients.ConclusionOur study provides an excellent nomogram for predicting the prognosis of CHGNEC patients. The prognostic nomogram can be a useful tool for clinicians in identifying high-risk patients and making personalized treatment decisions.