scholarly journals A Competing Risk Nomogram for Predicting Cancer-Specific Survival Among Patients with Prostate Cancer after Radical Prostatectomy

2020 ◽  
Author(s):  
Xianghong Zhou ◽  
Shi Qiu ◽  
Di Jin ◽  
Kun Jin ◽  
Xiaonan Zheng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Radical Prostatectomy ◽  
Risk Stratification ◽  
Marital Status ◽  
Cumulative Incidence ◽  
Validation Cohort ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Training Cohort

Abstract Background: We aimed to develop a detailed individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, Gleason Score biopsy. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray’s proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and GS biopsy were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812-0.844), and the C-index was 0.838 (%95CI, 0.813-0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve postoperative management of patients

scholarly journals Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xianghong Zhou ◽  
Shi Qiu ◽  
Kun Jin ◽  
Qiming Yuan ◽  
Di Jin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Radical Prostatectomy ◽  
Risk Stratification ◽  
Marital Status ◽  
Cumulative Incidence ◽  
Validation Cohort ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Training Cohort

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

scholarly journals Development and validation of a nomogram to predict cancer-specific survival of uveal melanoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qiaozhu Zeng ◽  
Yuou Yao ◽  
Mingwei Zhao
Keyword(s):  
Prognostic Factors ◽  
Uveal Melanoma ◽  
Validation Cohort ◽  
Staging System ◽  
Competing Risk ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Ajcc Staging System ◽  
Ajcc Staging

Abstract Background Uveal melanoma (UM) is a rare but aggressive cancer, which is the most common primary intraocular malignancy in adults. We aimed to develop and validate a competing risk nomogram to predict cancer-specific survival (CSS) of patients with UM, as well as compare its prognostic value with that of the American Joint Committee on Cancer (AJCC) staging system. Methods Data of patients diagnosed with UM from 2010 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We extracted and integrated significant prognostic factors based on competing risk regression to build a nomogram. The nomogram with an online prediction version was also created. The performance of the nomogram was evaluated using Harrell’s concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curve was carried out to estimate clinical applicability of the model. Improvements in the predictive accuracy of our new model compared with AJCC staging system were estimated by calculating the relative integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). Results A total of 839 eligible patients with primary UM were randomly assigned to a training cohort (588, 70%) and a validation cohort (251, 30%). Age, histological type, T stage and M stage were independent prognostic factors to predict CSS of UM and were incorporated in the nomogram. The calibration plots indicated that the 3- and 5-year CSS probabilities were consistent between the nomogram prediction and the actual observation. The C-index for this model was 0.778 (95% CI:0.756–0.800) and 0.786 (95% CI: 0.749–0.816) in the training cohort and validation cohort. Areas under the curve (AUCs) were 0.814, 0.771, and 0.792 in the training cohort, 0.788, 0.781 and 0.804 in the validation cohort, respectively. The NRI value in AJCC staging system was − 0.153 (95% CI -0.29 – − 0.041) for 3 years of follow-up and − 0.276 (95% CI -0.415 – − 0.132) for 5 years of follow-up. The IDI values for 3 and 5 years of follow-up in the AJCC staging system were − 0.021 (P = 0.076) and − 0.045 (P = 0.004), respectively. Conclusions We have developed and validated a competing risk nomogram to reliably predict cancer-specific survival of patients with UM. This convenient tool may be useful for evaluating cancer-specific prognosis.

scholarly journals Development and Validation of a Preoperative Nomogram for Predicting Survival of Patients with Locally Advanced Prostate Cancer after Radical Prostatectomy

2019 ◽  
Author(s):  
Xianghong Zhou ◽  
Qingyang Ning ◽  
Kun Jin ◽  
Tao Zhang ◽  
Xuelei Ma
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Advanced Prostate Cancer ◽  
Validation Cohort ◽  
Locally Advanced ◽  
Staging System ◽  
Locally Advanced Prostate Cancer ◽  
Training Cohort ◽  
Ajcc Staging System ◽  
Ajcc Staging

Abstract Background: For selected locally advanced prostate cancer (PCa) patients, radical prostatectomy (RP) is one of the first-line treatments. We aimed to develop a preoperative nomogram to identify what kinds of patients can get the most survival benefits after RP. Methods: We conducted analyses with data from the the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable Cox regression analyses were used to reveal prognostic factors. A nomogram was built by these factors and validated by concordance index (C-index) and calibration curves. Risk stratification was established based on the nomogram. Results: We studied 14185 patients. N stage, Gleason Score, and percent of positive cores were the independent prognostic factors used to construct the nomogram. For validating, in the training cohort, the C-index was 0.779 (95% CI 0.736–0.822), and in the validation cohort, the C-index was 0.773 (95% CI 0.718–0.808). Calibration curves showed that the predicted survival and actual survival were very close. The nomogram performed better over the American Joint Committee on Cancer (AJCC) staging system (C-index 0.779 versus 0.763 for training cohort, and 0.773 versus 0.745 for validation cohort). The new stratification of risk groups based on the nomogram also showed better discrimination than the AJCC staging system. Conclusions: The preoperative nomogram can provide favorable prognosis stratification ability to help clinicians identify patients who are suitable for surgery.

scholarly journals MP40-13 PROGNOSTIC FACTORS AND ONCOLOGIC OUTCOMES AFTER ROBOT-ASSISTED RADICAL PROSTATECTOMY IN PT3B PROSTATE CANCER

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Dae Keun Kim ◽  
Atalla Alatawi ◽  
Abulhasan Sheikh ◽  
Ibrahim Alabdulaali ◽  
Ali Abdel Raheem ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Radical Prostatectomy ◽  
Oncologic Outcomes ◽  
Robot Assisted

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

2021 ◽  
Vol 16 ◽  
Author(s):  
Jin-Guo Chen ◽  
Jing-Quan Wang ◽  
Tian-Wen Peng ◽  
Zhe-Sheng Chen ◽  
Shan-Chao Zhao
Keyword(s):  
Prognostic Factors ◽  
Validation Cohort ◽  
Early Stage ◽  
Predictive Ability ◽  
Testicular Germ Cell Tumor ◽  
Progression Free Survival ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

scholarly journals Prognostic Nutritional Index as an independent prognostic factor in locoregionally advanced squamous cell head and neck cancer

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000425 ◽  
Author(s):  
Gema Bruixola ◽  
Javier Caballero ◽  
Federica Papaccio ◽  
Angelica Petrillo ◽  
Aina Iranzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Prognostic Nutritional Index ◽  
Training Cohort ◽  
Lymphocyte Ratio ◽  
Nutritional Index

BackgroundLocally advanced head and neck squamous cell carcinoma (LAHNSCC) is a heterogeneous disease in which better predictive and prognostic factors are needed. Apart from TNM stage, both systemic inflammation and poor nutritional status have a negative impact on survival.MethodsWe retrospectively analysed two independent cohorts of a total of 145 patients with LAHNSCC treated with induction chemotherapy followed by concurrent chemoradiotherapy at two different academic institutions. Full clinical data, including the Prognostic Nutritional Index (PNI), neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio, were analysed in a training cohort of 50 patients. Receiver operating characteristic curve analysis was used to establish optimal cut-off. Univariate and multivariate analyses of prognostic factors for overall survival (OS) were performed. Independent predictors of OS identified in multivariate analysis were confirmed in a validation cohort of 95 patients.ResultsIn the univariate analysis, low PNI (PNI<45) (p=0.001), large primary tumour (T4) (p=0.044) and advanced lymph node disease (N2b-N3) (p=0.025) were significantly associated with poorer OS in the validation cohort. The independent prognostic factors in the multivariate analysis for OS identified in the training cohort were dRNL (p=0.030) and PNI (p=0.042). In the validation cohort, only the PNI remained as independent prognostic factor (p=0.007).ConclusionsPNI is a readily available, independent prognostic biomarker for OS in LAHNSCC. Adding PNI to tumour staging could improve individual risk stratification of patients with LAHNSCC in future clinical trials.

scholarly journals Pre-treatment risk stratification of prostate cancer patients:A critical review

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
George Rodrigues ◽  
Padraig Warde ◽  
Tom Pickles ◽  
Juanita Crook ◽  
Michael Brundage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Gleason Score ◽  
Critical Review ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Original Research ◽  
Pre Treatment

Introduction:  The use of accepted prostate cancer risk stratification groups based on prostate-specific antigen, T stage and Gleason score assists in therapeutic treatment decision-making, clinical trial design and outcome reporting. The utility of integrating novel prognostic factors into an updated risk stratification schema is an area of current debate. The purpose of this work is to critically review the available literature on novel pre-treatment prognostic factors and alternative prostate cancer risk stratification schema to assess the feasibility and need for changes to existing risk stratification systems. Methods:  A systematic literature search was conducted to identify original research publications and review articles on prognostic factors and risk stratification in prostate cancer. Search terms included risk stratification, risk assessment, prostate cancer or neoplasms, and prognostic factors. Abstracted information was assessed to draw conclusions regarding the potential utility of changes to existing risk stratification schema. Results:  The critical review identified three specific clinically relevant potential changes to the most commonly used three-group risk stratification system: (1) the creation of a very-low risk category; (2) the splitting of intermediate-risk into a low- and highintermediate risk groups; and (3) the clarification of the interface between intermediate- and high-risk disease. Novel pathological factors regarding high-grade cancer, subtypes of Gleason score 7 and percentage biopsy cores positive were also identified as potentially important risk-stratification factors. Conclusions:  Multiple studies of prognostic factors have been performed to create currently utilized prostate cancer risk stratification systems. We propose potential changes to existing systems.

Liver regeneration after radiotherapy in patients with hepatocellular carcinoma: A simple-to-use model and nomogram.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16115-e16115
Author(s):  
Ting-Shi Su ◽  
Li-Qing Li ◽  
Shi-Xiong Liang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Stratification ◽  
Liver Regeneration ◽  
Validation Cohort ◽  
Liver Volume ◽  
Normal Liver ◽  
Target Area ◽  
Good Prediction ◽  
Characteristic Analysis ◽  
Training Cohort

e16115 Background: In the past clinical practice of radiotherapy for liver cancer, liver regeneration (LR) which is beneficial to the prevention or recovery of radiation-induced liver injury, has not received enough attention. In current study, we aimed to build and validate multivariate model for liver regeneration after radiation therapy for hepatocellular carcinoma (HCC) based on data from 2 prospective studies. Methods: Thirty patients treated with preoperative downstaging radiotherapy were prospectively included in the training cohort, and 21 patients treated with postoperative adjuvant radiotherapy were included in the validation cohort. Liver regeneration was defined as an increase of more than 10% of normal liver volume in the areas of the protected hepatic segment or lobe, without Child-Pugh class decreased and tumor progression compared to pre-radiotherapy. Model and nomogram of liver regeneration after radiotherapy were developed and validated. The cut-off points of each optimal predictors were obtained using receiver-operating characteristic analysis. Risk stratification based on the cut-off point was conducted to compare the proportion of patients with liver regeneration between subgroups. Results: After radiotherapy, 12 (40%) cases in the training cohort and 13 (61.9%) cases in the validation cohort experienced liver regeneration. The model and nomogram of liver regeneration based on SVs20 (standard residual liver volume spared from at least 20 Gy) and alanine aminotransferase (ALT) showed good prediction performance (AUC = 0.759) in training cohort and performed well (AUC = 0.808) in the validation cohort. The risk stratification according to the cutoffs of SVs20 with 303.4 mL and ALT with 43 U/L demonstrated clear differentiation in risk of liver regeneration between the training(P = 0.049) and entire cohort (P = 0.032). The proportion of patients with liver regeneration decrease progressively with 88.9% in high-probability group (ALT＜43 U/L and SVs20＜303.4 mL), 60% in high-intermediate probability group (ALT ≥43 U/L and SVs20＜303.4 mL), 43.75% in low-intermediate probability group (ALT＜43 U/L and SVs20≥303.4 mL) and 33% in low- probability group (ALT≥43 U/L and SVs20≥303.4 mL). Conclusions: SVs20 and ALT are optimal predictors for liver regeneration. This simple-to-use nomogram is beneficial to the constraints of normal liver outside the radiotherapy target area and make prognosis-based decision without complex calculations. Clinical trial information: ChiCTR1800015350. [Table: see text]

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