Prediction of distant metastasis of spinal and pelvic osteosarcoma by constructing and validating nomogram:a retrospective study based on SEER database

Abstract Background:Spinal and pelvic osteosarcoma is a rare type of all osteosarcomas,and distant metastasis is an important factor for poor prognosis of this disease. There are no similar studies on prediction of distant metastasis of spinal and pelvic osteosarcoma. We aim to construct and validate a nomogram to predict the risk of distant metastasis of spinal and pelvic osteosarcoma.Methods:We collected the data on patients with spinal and pelvic osteosarcoma from the Surveillance, Epidemiology, and End Results(SEER) database retrospectively. The Kaplan-Meier curve was used to compare differences in survival time between patients with metastasis and non-metastasis. Total patients were randomly divided into training cohort and validation cohort. The risk factor of distant metastasis were identified via the least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic analysis. The nomogram we constructed were validated internally and externally by C-index, calibration curves,receiver operating characteristic(ROC) curve and Decision curve analysis (DCA).Results:The Kaplan-Meier curve showed that the survival time of non-metastatic patients was longer than that of metastatic patients(P＜0.001).All patients(n=358) were divided into training cohort(n=269) and validation cohort(n=89).The LASSO regression selected five meaningful variables in the training cohort. The multivariate logistic regression analysis demonstrated that surgery(yes,OR=0.175, 95%CI=0.095-0.321,p=0.000) was the independent risk factors for distant metastasis of patients with spinal and pelvic osteosarcoma. The C-index and calibration curves showed the good agreement between the predicted results and the actual results. The area under the receiver operating characteristic curve(AUC) values were 0.748(95%CI=0.687-0.817) and 0.758(95%CI=0.631-0.868) in the training and validation cohorts respectively. The DCA showed that the nomogram has a good clinical usefulness and net benefit.Conclusion:No surgery is the independent risk factor of distant metastasis of spinal and pelvic osteosarcoma. The nomogram we constructed to predict the probability of distant metastasis of patients with spinal and pelvic osteosarcoma is reliable and effective by internal and external verification.

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A Systemic Inflammation Response Score for Prognostic Prediction of Breast Cancer Patients Undergoing Surgery

Journal of Personalized Medicine ◽

10.3390/jpm11050413 ◽

2021 ◽

Vol 11 (5) ◽

pp. 413

Author(s):

Kaiming Zhang ◽

Liqin Ping ◽

Xueqi Ou ◽

Meiheban Bazhabayi ◽

Xiangsheng Xiao

Keyword(s):

Breast Cancer ◽

Receiver Operating Characteristic ◽

Cancer Patients ◽

Clinical Features ◽

Operating Characteristic ◽

Validation Cohort ◽

Curve Analysis ◽

Breast Cancer Patients ◽

Training Cohort ◽

Response Score

Background: Systemic inflammatory response is related to the occurrence, progression, and prognosis of cancers. In this research, a novel systemic inflammation response score (SIRS) was calculated, and its prognostic value for postoperative stage I-III breast cancer (BC) patients was analyzed. Methods: 1583 BC patients were included in this research. Patients were randomly divided into a training cohort (n = 1187) and validation cohort (n = 396). SIRS was established in the training cohort based on independent prognostic hematological indicator, its relationship between prognosis and clinical features was analyzed. Then, a nomogram consisted of SIRS and clinical features was established, its performance was examined by calibration plots and receiver operating characteristic curve analysis. Results: The SIRS was an independent prognostic indicator for BC patients, and a high-SIRS was related to multifocality, advanced N stage, and worse prognosis. Incorporating SIRS into a nomogram could accurately predict the prognosis of BC patients, the results of receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of nomogram was up to 0.806 in training cohort and 0.905 in the validation cohort. Conclusion: SIRS was associated with the prognosis of patients with breast cancer. Nomogram based on SIRS can accurately predict the prognosis of breast cancer patients.

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Temporal Stability and Prognostic Biomarker Potential of the Prostate Cancer Urine miRNA Transcriptome

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djz112 ◽

2019 ◽

Vol 112 (3) ◽

pp. 247-255 ◽

Cited By ~ 4

Author(s):

Jouhyun Jeon ◽

Ekaterina Olkhov-Mitsel ◽

Honglei Xie ◽

Cindy Q Yao ◽

Fang Zhao ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Confidence Interval ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Validation Cohort ◽

Machine Learning Techniques ◽

Training Cohort ◽

Learning Techniques ◽

Receiver Operating

Abstract Background The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. Methods We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). Results We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). Conclusions These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.

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Construction and validation of a nomogram for predicting cancer-specific survival in hepatocellular carcinoma patients

Scientific Reports ◽

10.1038/s41598-020-78545-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kang Liu ◽

Gaobo Huang ◽

Pengkang Chang ◽

Wei Zhang ◽

Tao Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Operating Characteristic ◽

Validation Cohort ◽

Seer Database ◽

Concordance Index ◽

Cancer Specific Survival ◽

Discrimination Power ◽

Training Cohort ◽

Good Agreement

AbstractThe prognosis of patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a research hotspot. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of patients with HCC and ICC. We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in HCC patients. The nomogram was validated by concordance index (C-index), the receiver operating characteristic (ROC) curve and calibration curves. A total of 3227 patients diagnosed with HCC (3038) and ICC (189) between 2010 and 2015 were included in this study. The C-index of the nomogram for HCC patients was 0.790 in the training cohort and 0.806 in the validation cohort. The 3- and 5-year AUCs were 0.811 and 0.793 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in HCC patients. We have confirmed the precise calibration and excellent discrimination power of our nomogram.

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A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

Journal of Perioperative Practice ◽

10.1177/1750458919875592 ◽

2019 ◽

Vol 30 (7-8) ◽

pp. 221-228

Author(s):

Shahab Hajibandeh ◽

Shahin Hajibandeh ◽

Nicholas Hobbs ◽

Jigar Shah ◽

Matthew Harris ◽

...

Keyword(s):

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Validation Cohort ◽

Characteristic Curve ◽

Curve Analysis ◽

Emergency Laparotomy ◽

Contamination Index ◽

Operating Characteristic Curve ◽

Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

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Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer patients with liver metastases or other metastatic patterns.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.3_suppl.435 ◽

2021 ◽

Vol 39 (3_suppl) ◽

pp. 435-435

Author(s):

Junjie Hang ◽

Lixia Wu

Keyword(s):

Pancreatic Cancer ◽

Liver Metastases ◽

Cancer Patients ◽

Validation Cohort ◽

Advanced Pancreatic Cancer ◽

Region Of Interest ◽

Calibration Plot ◽

Training Cohort ◽

Kaplan Meier ◽

Metastatic Patterns

435 Background: Pancreatic cancer patients with liver metastases had much poorer prognosis than those with other metastatic patterns. This study aimed to develop and validate a radiomics model to discriminate pancreatic cancer patients with liver metastases from patients with other metastatic patterns. Methods: We evaluated 77 patients advanced pancreatic cancer (APC) with different metastatic patterns and performed texture analysis on the region of interest (ROI). 58 patients and 19 patients were allocated randomly into the training cohort and the validation cohort with almost the same proportion of patients with liver metastases. An independent samples t-test was used for initial feature selection in the training cohort. Random Forest Classifier (RFC) was used to construct models based on these features in both cohorts and a radiomics signature (RS) was derived from the model. Then a nomogram was constructed based on RS and CA19-9, and validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. Results: A nomogram based on the RS and CA19-9 was constructed and it demonstrated good discrimination in the training cohort (AUC = 0.93) and validation cohort (AUC = 0.81). Kaplan-meier methods showed that patients with RS>0.61 had much poorer OS than patients with RS < 0.61 in both cohorts. Conclusions:This study presents a radiomics nomogram incorporating both RS and CA19-9, which can be used to discriminate advanced pancreatic cancer patients with liver metastases from patients with other metastatic patterns.

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AGLR is a Novel Index for the Prognosis of Hepatocellular Carcinoma Patients: A Retrospective Study

10.21203/rs.3.rs-103565/v1 ◽

2020 ◽

Author(s):

Yan Liao ◽

Rongyu Wei ◽

Renzhi Yao ◽

Liling Qin ◽

Jun Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Validation Cohort ◽

Multivariate Analyses ◽

The Novel ◽

Gamma Glutamyl Transpeptidase ◽

Roc Curve Analysis ◽

Training Cohort ◽

Scoring Model ◽

Kaplan Meier

Abstract Background: Most hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase) / lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. Methods: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method.Results: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with α-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI, 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI, 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis.Conclusions: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

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Metabolic Syndrome and Its Components Are Associated With Hypoxemia After Surgery for Acute Type A Aortic Dissection: an Observational Study

10.21203/rs.3.rs-900428/v1 ◽

2021 ◽

Author(s):

Like Zhang ◽

Lei Zhang ◽

Zengren Zhao ◽

Yun Liu ◽

Juzeng Wang ◽

...

Keyword(s):

Metabolic Syndrome ◽

Regression Analysis ◽

Risk Factor ◽

Aortic Dissection ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Type A ◽

Acute Type ◽

Type A Aortic Dissection ◽

Receiver Operating

Abstract Background: The aim of this study was to explore whether or to what extent metabolic syndrome (METs) and its components were associated with hypoxemia in acute type A aortic dissection (ATAAD) patients after surgery.Methods: This study involved 271 inpatients who underwent surgery. Demographic and clinical data were collected. Subgroup analysis, mixed model regression analysis, and receiver operating characteristic (ROC) curve analysis were performed, and a scoring system was evaluated.Results: The 271 inpatients were assigned to the hypoxemia group (n = 48) or no hypoxemia group (n = 223) regardless of METs status. Compared to the no hypoxemia group, the hypoxemia group had a higher incidence of METs. Hypoxemia was present in 0%, 3.7%, 19.8%, 51.5%, 90.0% and 100% in the groups of individuals who met the diagnostic criteria of MetS 0, 1, 2, 3, 4 and 5 times, respectively. In the multivariable logistic regression analysis, BMI quartile was still a risk factor for hypoxemia after adjustment for other risk factors. After adjustment for potential confounding factors, METs was an independent risk factor for hypoxemia in several models. After assigning a score for each METs component present, the AUCs were 0.852 (95% CI: 0.789–0.914) in all patients, 0.728 (95% CI: 0.573–0.882) in patients with METs and 0.744 (95% CI: 0.636–0.853) in patients without METs according to receiver operating characteristic analysis.Conclusions: METs, especially body mass index, confers a greater risk of hypoxemia in ATAAD after surgery.

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Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Frontiers in Medicine ◽

10.3389/fmed.2021.680679 ◽

2021 ◽

Vol 8 ◽

Author(s):

Haisheng You ◽

Mengmeng Teng ◽

Chun Xia Gao ◽

Bo Yang ◽

Sasa Hu ◽

...

Keyword(s):

Decision Making ◽

Prognostic Factors ◽

Elderly Patients ◽

Clinical Decision Making ◽

Cox Regression ◽

Validation Cohort ◽

Discrimination Performance ◽

Seer Database ◽

Histologic Subtype ◽

Training Cohort

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

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Alpha-Fetoprotein Ratio Predicts Alpha-Fetoprotein Positive Hepatocellular Cancer Patient Prognosis after Hepatectomy

Disease Markers ◽

10.1155/2022/7640560 ◽

2022 ◽

Vol 2022 ◽

pp. 1-9

Author(s):

Li-Yue Sun ◽

Wen-Jian Cen ◽

Wen-Ting Tang ◽

Ling Deng ◽

Fang Wang ◽

...

Keyword(s):

Validation Cohort ◽

Disease Free Survival ◽

Hepatocellular Cancer ◽

Alpha Fetoprotein ◽

Free Survival ◽

Training Cohort ◽

Kaplan Meier ◽

Significant Independent Risk Factor ◽

Patient Prognosis

Background. This study was conducted to investigate the effect of alpha-fetoprotein (AFP) ratio on the prognosis of AFP-positive hepatocellular carcinoma (HCC) patients after hepatectomy. Methods. We retrospectively included 879 HCC patients with AFP-positive who underwent hepatectomy from February 2012 to October 2017 and randomly divided into training cohort and validation cohort. AFP ratio was equal to the AFP level within one week before hepatectomy to AFP level within 20-40 days after surgery. The end point of follow-up was disease-free survival (DFS) and overall survival (OS). Results. AFP ratio was not associated with clinical characteristics in training cohort and validation cohort. According to the X-tile software, the optimum cut-off point was 17.8 for AFP ratio. Significant differences between AFP ratio high and AFP ratio low were observed in DFS and OS in both cohort ( p < 0.05 ). Kaplan-Meier curves and receiver-operating curves were showed that AFP ratio was better than AFP level preoperation in predicting the prognosis of AFP-positive HCC patients after hepatectomy. The multivariate analysis demonstrated that AFP ratio was a significant independent risk factor for both OS and DFS in HCC patients with AFP-positive. Conclusions. AFP ratio might be a prognosis predictor for HCC patients with AFP-positive after hepatectomy.

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Prognostic Nomogram for Postoperative Patients With Gastroesophageal Junction Cancer of No Distant Metastasis

Frontiers in Oncology ◽

10.3389/fonc.2021.643261 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiang Guo ◽

YuanYuan Peng ◽

Heng Yang ◽

JiaLong Guo

Keyword(s):

Regression Analysis ◽

Surgical Resection ◽

Distant Metastasis ◽

Calibration Curve ◽

Cox Regression ◽

Validation Cohort ◽

Predictive Accuracy ◽

Gastroesophageal Junction ◽

Training Cohort ◽

Cox Regression Analysis

BackgroundGastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.MethodsThe 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.Results2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.ConclusionsNomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

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