Prediction Model For Overall Survival Research Articles

A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study.

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

Developing and Validating Novel Nomograms for Predicting the Overall Survival and Cancer-Specific Survival of Patients With Primary Vulvar Squamous Cell Cancer.

Background: To develop and validate novel nomograms for better predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with vulvar squamous cell cancer (VSCC).Methods: A retrospective analysis using a population-based database between 2004 and 2016 was carried. A 10-fold cross-validation with 200 repetitions was used to choose the best fit multivariate Cox model based on the net-benefit of decision curve analysis. Net-benefit, Harrell's C concordance statistic (C-statistic) of calibration plot, and area under the receiver operating characteristic curve (AUC) were used to evaluate the model prediction accuracy. Nomograms of the OS and CSS were generated based on the best fit model.Results: Of the 6,792 patients with VSCC, 5,094 (75%) and 1,698 (25%) were allocated to the training and validation cohort, respectively. All the variables were balanced between the training and validation cohorts. Age, insurance, tumor size, pathological grade, radiotherapy, chemotherapy, invasion depth, lymphadenectomy, sentinel lymph nodes biopsy, surgery, N stage, and M stage were in the best fit model for generating nomograms. The decision curve analysis, calibration plot, and receiver operating characteristic (ROC) curve show the better prediction performance of the model compared to previous studies. The C-statistics of our model for OS prediction are 0.80, 0.83, and 0.81 in the training, validation, and overall cohorts, respectively, while for CSS prediction are 0.83, 0.85, and 0.84. The AUCs for 3- and 5-year OS are the same and are 0.81, 0.83, and 0.81 in the training, validation, and overall cohorts, respectively. The AUCs for 3- and 5-year CSS are 0.78 and 0.80, 0.79 and 0.80, and 0.79 and 0.80 in those three cohorts.Conclusions: Our model shows the best prediction accuracy of the OS and CSS for patients with vulvar cancer (VC), which is of significant clinical practice value.

Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis.

Prognosis of high-risk neuroblastoma (HRNB) remains poor despite multimodal therapies. Better prediction of survival could help to refine patient stratification and better tailor treatments. We established a mechanistic model of metastasis in HRNB relying on two processes: growth and dissemination relying on two patient-specific parameters: the dissemination rate μ and the minimal visible lesion size Svis. This model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels, and the meta-iodobenzylguanidine International Society for Paediatric Oncology European (SIOPEN) score from nuclear imaging, using data from 49 metastatic patients. It was able to describe the data of total tumor mass (lactate dehydrogenase, R2 > 0.99) and number of visible metastases (SIOPEN, R2 = 0.96). A prediction model of overall survival (OS) was then developed using Cox regression. Clinical variables alone were not able to generate a model with sufficient OS prognosis ability (P = .507). The parameter μ was found to be independent of the clinical variables and positively associated with OS (P = .0739 in multivariable analysis). Critically, addition of this computational biomarker significantly improved prediction of OS with a concordance index increasing from 0.675 (95% CI, 0.663 to 0.688) to 0.733 (95% CI, 0.722 to 0.744, P < .0001), resulting in significant OS prognosis ability (P = .0422).

Model Development of CDK4/6 Predicted Efficacy in Patients With Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced or Metastatic Breast Cancer.

Three cyclin-dependent kinase 4/6 inhibitors (CDKIs) are approved by the US Food and Drug Administration for the treatment of patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced or metastatic breast cancer in combination with hormonal therapy (HT). We hypothesized that on an individual basis, efficacy outcomes and adverse event (AE) development can be predicted using baseline patient and tumor characteristics. Individual-level data from seven randomized controlled trials submitted to the US Food and Drug Administration for new or supplemental marketing applications of CDKIs were pooled. Progression-free survival (PFS), overall survival (OS), and AE prediction models were developed for specific treatment regimens (HT v HT plus CDKI). An individual's characteristics were used in all models simultaneously to create a group of predicted outcomes that are comparable across treatment settings. Accuracy of the PFS and OS prediction models for HT were 66% and 64%, respectively, with the strongest predictors being menopausal status and therapy line. The corresponding AE prediction models resulted in an average area under the curve of 0.613. Accuracy of the PFS and OS prediction models for HT plus CDKI were 62% and 63%, respectively, with the strongest predictors being histologic grade for both. The corresponding AE prediction models resulted in an average area under the curve of 0.639. This exploratory analysis demonstrated that models of efficacy outcomes and AE development can be developed using baseline patient and tumor characteristics. Comparison of paired models can inform treatment selection for individuals on the basis of the patient's personalized goals and concerns. Although use of CDKIs is standard of care in the first- or second-line setting, this model provides prognostic information that may inform individual treatment decisions.

Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (ccRCC).

PurposeThis study aims to carry out a pan-cancer analysis of kinesin family member 23 (KIF23) and construct a predictive model for the prognosis of clear cell renal cell carcinoma (ccRCC) patients.MethodsWe evaluated the differential expression of KIF23 in pan-cancer by The Cancer Genome Atlas (TCGA) and Oncomine database. Then, the correlation between KIF23 with prognosis, clinical grade, stage, immune subtype, tumor mutation burden (TMB), microsatellite instability (MSI) and immune microenvironment was explored by TCGA, an integrated repository portal for tumor-immune system interactions (TISIDB) and cBioPortal. Subsequently, we screened out ferroptosis-related genes (FRGs) related to KIF23 and constructed a risk score model. Univariate Cox analysis was used to determine independent prognostic factors for ccRCC overall survival (OS), and a nomogram was established. Furthermore, gene set enrichment analysis (GSEA) was applied to study the biological functions and pathways of KIF23. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out to evaluate the expression of KIF23.ResultsKIF23 was highly expressed in most tumors. Further, KIF23 was strongly correlated with prognosis, clinical grade, stage, immune subtype, TMB, MSI and immune microenvironment in different tumors. We found that KIF23 was significantly associated with all aspects of ccRCC. Then, 8 FRGs were identified to construct a risk score model together with KIF23. And a prognostic nomogram prediction model of OS was established. After GSEA analysis, cell cycle, condensed chromosome and other physiological processes were screened out. Finally, qRT-PCR verified the high expression of KIF23 in ccRCC cell lines than normal kidney cell line.ConclusionKIF23 may act as a pivotal part in occurrence and progression of different tumors. In ccRCC, KIF23 can be a great prognostic biomarker, and the nomogram based on KIF23 may contribute to better treatment plans for ccRCC patients.

Nomogram to predict overall survival based on the log odds of positive lymph nodes for patients with endometrial carcinosarcoma after surgery

PurposeAims to compare the prognostic performance of the number of positive lymph nodes (PLNN), lymph node ratio (LNR) and log odds of metastatic lymph nodes (LODDS) and establish a prognostic nomogram to predict overall survival (OS) rate for patients with endometrial carcinosarcoma (ECS).MethodsPatients were retrospectively obtained from Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2015. The prognostic value of PLNN, LNR and LODDS were assessed. A prediction model for OS was established based on univariate and multivariate analysis of clinical and demographic characteristics of ECS patients. The clinical practical usefulness of the prediction model was valued by decision curve analysis (DCA) through quantifying its net benefits.ResultsThe OS prediction accuracy of LODDS for ECS is better than that of PLNN and LNR. Five factors, age, tumor size, 2009 FIGO, LODDS and peritoneal cytology, were independent prognostic factors of OS. The C-index of the nomogram was 0.743 in the training cohort. The AUCs were 0.740, 0.682 and 0.660 for predicting 1-, 3- and 5-year OS, respectively. The calibration plots and DCA showed good clinical applicability of the nomogram, which is better than 2009 FIGO staging system. These results were verified in the validation cohort. A risk classification system was built that could classify ECS patients into three risk groups. The Kaplan-Meier curves showed that OS in the different groups was accurately differentiated by the risk classification system and performed much better than FIGO 2009.ConclusionOur results indicated that LODDS was an independent prognostic indicator for ECS patients, with better predictive efficiency than PLNN and LNR. A novel prognostic nomogram for predicting the OS rate of ECS patients was established based on the population in the SEER database. Our nomogram based on LODDS has a more accurate and convenient value for predicting the OS of ECS patients than the FIGO staging system alone.

A gene expression–based model predicts outcome in children with intermediate-risk classical Hodgkin lymphoma

AbstractClassical Hodgkin lymphoma (cHL) is a common malignancy in children and adolescents. Although cHL is highly curable, treatment with chemotherapy and radiation often come at the cost of long-term toxicity and morbidity. Effective risk-stratification tools are needed to tailor therapy. Here, we used gene expression profiling (GEP) to investigate tumor microenvironment (TME) biology, to determine molecular correlates of treatment failure, and to develop an outcome model prognostic for pediatric cHL. A total of 246 formalin-fixed, paraffin-embedded tissue biopsies from patients enrolled in the Children's Oncology Group trial AHOD0031 were used for GEP and compared with adult cHL data. Eosinophil, B-cell, and mast cell signatures were enriched in children, whereas macrophage and stromal signatures were more prominent in adults. Concordantly, a previously published model for overall survival prediction in adult cHL did not validate in pediatric cHL. Therefore, we developed a 9-cellular component model reflecting TME composition to predict event-free survival (EFS). In an independent validation cohort, we observed a significant difference in weighted 5-year EFS between high-risk and low-risk groups (75.2% vs 90.3%; log-rank P = .0138) independent of interim response, stage, fever, and albumin. We demonstrate unique disease biology in children and adolescents that can be harnessed for risk-stratification at diagnosis. This trial was registered at www.clinicaltrials.gov as #NCT00025259.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data.

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

Development and External Validation of a Model to Predict Overall Survival in Patients With Resected Gallbladder Cancer.

The aim of this study was to develop and validate a clinical prediction model to predict overall survival in patients with nonmetastatic, resected gallbladder cancer (GBC). Although several tools are available, no optimal method has been identified to assess survival in patients with resected GBC. Data from a Dutch, nation-wide cohort of patients with resected GBC was used to develop a prediction model for overall survival. The model was internally validated and a cohort of Australian GBC patients who underwent resection was used for external validation. The performance of the American Joint Committee on Cancer (AJCC) staging system and the present model were compared. In total, 446 patients were included; 380 patients in the development cohort and 66 patients in the validation cohort. In the development cohort median survival was 22 months (median follow-up 75 months). Age, T/N classification, resection margin, differentiation grade, and vascular invasion were independent predictors of survival. The externally validated C-index was 0.75 (95%CI: 0.69-0.80), implying good discriminatory capacity. The discriminative ability of the present model after internal validation was superior to the ability of the AJCC staging system (Harrell C-index 0.71, [95%CI: 0.69-0.72) vs. 0.59 (95% CI: 0.57-0.60)]. The proposed model for the prediction of overall survival in patients with resected GBC demonstrates good discriminatory capacity, reasonable calibration and outperforms the authoritative AJCC staging system. This model can be a useful tool for physicians and patients to obtain information about survival after resection and is available from https:// gallbladderresearch.shinyapps.io/Predict_GBC_survival/.

Nomograms-based prediction of overall and cancer-specific survivals for patients diagnosed with major salivary gland carcinoma

BackgroundMajor salivary glands carcinoma (MSGC) is a relatively rare cancer with diverse histological types and biological behavior. The treatment planning and prognosis prediction are challenging for clinicians. The aim of the current study was to establish a reliable and effective nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) for MSGC patients.MethodsPatients pathologically diagnosed with MSGC were recruited from Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups (7:3 ratio). Univariate, multivariate Cox proportional hazard models, and least absolute shrinkage and selection operator (LASSO) regression were adopted for the selection of risk factors. Nomograms were developed using R software. The model performance was evaluated by drawing receiver operating characteristic (ROC), overtime C-index curves, and calibration curves. Harrell C-index, areas under the curves (AUC), and Brier score were also calculated. The decision curve analysis (DCA) was conducted to measure the net clinical benefit.ResultsA total of 11,362 patients were identified and divided into training (n=7,953) and validation (n=3,409) dataset. Sex, age, race, marital status, site, differentiation grade, American Joint Committee on Cancer (AJCC) stage, T/N/M stage, tumor size, surgery, and histological type were incorporated into the Cox hazard model for OS prediction after variable selection, while all predictors, except for marital status and site, were selected for CSS prediction. For 5-year prediction, the AUC of the nomogram for OS and CSS was 83.5 and 82.7 in the training and validation dataset, respectively. The C-index was 0.787 for OS and 0.798 for CSS in the validation group. The Brier score was 0.0153 and 0.0130 for OS and CSS, respectively. The calibration curves showed that the nomogram had well prediction accuracy. From the perspective of DCA, a nomogram was superior to the AJCC stage and TNM stage in net benefit. In general, the performance of the nomogram was consistently better compared to the AJCC stage and TNM stage across all settings.ConclusionsThe performance of the novel nomogram for predicting OS and CSS of MSGC patients was further verified, revealing that it could be used as a valuable tool in assisting clinical decision-making.

Prediction of survival in patients with advanced, refractory colorectal cancer in treatment with trifluridine/tipiracil: real-world vs clinical trial data

Trifluridine/tipiracil increases overall survival (OS) in patients with refractory, metastatic colorectal cancer (mCRC). A post hoc exploratory analysis of the RECOURSE randomized clinical trial (RCT) established two categories, a good prognosis corresponding to subjects having a low tumor burden and indolent disease. Other models in refractory mCRC are the FAS-CORRECT and Colon Life nomogram. The main objective was to externally validate the prognostic factors of the RECOURSE and FAS-CORRECT trials, and the Colon Life nomogram in a multicenter, real-world series of mCRC treated in 3rd and successive lines with trifluridine/tipiracil. The secondary aim was to develop an OS predictive model, TAS-RECOSMO. Between 2016 and 2019, 244 patients were recruited. Median OS was 8.15 vs 8.12 months for the poor (85% of the subjects) and good (15%) prognosis groups from the RESOURCE trial, respectively, log-rank p = 0.9. The most common grade 3–4 toxicities were neutropenia (17%), asthenia (6%), and anemia (5%). The AFT lognormal model TAS-RECOSMO included six variables: ECOG-PS, KRAS/NRAS/BRAF mutation status, time between diagnosis of metastasis and beginning of trifluridine/tipiracil, NLR, CEA, and alkaline phosphatase. The model’s bootstrapped bias-corrected c-index was 0.682 (95% CI, 0.636–0.722). The factors from the Colon Life model, FAS-CORRECT, and RECOURSE displayed a c-index of 0.690, 0.630, and 0.507, respectively. TAS-RECOSMO, FAS-CORRECT, and the Colon Life nomogram appear to predict OS in patients with refractory mCCR who begin trifluridine/tipiracil treatment in the real world. The prognostic groups of the RECOURCE RCT were unable to capture the situation of real-world subjects treated with trifluridine/tipiracil in this series.

Overexpression of RIPK4 Predicts Poor Prognosis and Promotes Metastasis in Ovarian Cancer.

This study was conducted to evaluate the prognostic value of receptor-interacting protein kinase 4 (RIPK4) in ovarian cancer (OC) and its role in tumorigenesis. RNA expression and the corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The relationship between clinical-pathological characteristics and RIPK4 expression was analyzed using the Wilcoxon signed-rank test and logistic regression. The Cox regression and the Kaplan-Meier method were used to evaluate the relationship between clinicopathological features and overall survival (OS). Gene set enrichment analysis (GSEA) was performed using Molecular Signatures Database. Scratch assay, transwell assay, and cell transfection were used to verify the function of RIPK4. Overexpression of RIPK4 was associated with the stage of OC and distant metastasis. Survival analysis revealed that patients with OC and higher expression of RIPK4 had a poorer prognosis. Univariate and multivariate analyses indicated that high expression of RIPK4 was associated with poor OS, as well as age and stage of OC. The areas under the curve (AUC) at 1, 4, and 8 years were 0.737, 0.634, and 0.669, respectively, according to the established OS prediction model. GSEA revealed that adherens junction, cadherin binding, and Wnt signaling pathway were enriched in the high RIPK4 expression group. Cell transfection confirmed RIPK4 was involved in the Wnt signaling pathway. RIPK4 can act as a potential prognostic molecular marker for poor survival in OC. Moreover, RIPK4 is associated with tumor metastasis and implicated in the regulation of the Wnt signaling pathway.

Development of an Eight-gene Prognostic Model for Overall Survival Prediction in Patients with Hepatocellular Carcinoma.

Background and AimsThe overall survival (OS) of hepatocellular carcinoma (HCC) remains dismal. Bioinformatic analysis of transcriptome data could identify patients with poor OS and may facilitate clinical decision. This study aimed to develop a prognostic gene model for HCC.MethodsGSE14520 was retrieved as a training set to identify differential expressed genes (DEGs) between tumor and adjacent liver tissues in HCC patients with different OS. A DEG-based prognostic model was then constructed and the TCGA-LIHC and ICGC-LIRI datasets were used to validate the model. The area under the receiver operating characteristic curve (AUC) and hazard ratio (HR) of the model for OS were calculated. A model-based nomogram was established and verified.ResultsIn the training set, differential expression analysis identified 80 genes dysregulated in oxidation-reduction and metabolism regulation. After univariate Cox and LASSO regression, eight genes (LPCAT1, DHRS1, SORBS2, ALDH5A1, SULT1C2, SPP1, HEY1 and GOLM1) were selected to build the prognostic model. The AUC for 1-, 3- and 5-year OS were 0.779, 0.736, 0.754 in training set and 0.693, 0.689, 0.693 in the TCGA-LIHC validation set, respectively. The AUC for 1- and 3-year OS were 0.767 and 0.705 in the ICGC-LIRI validation set. Multivariate analysis confirmed the model was an independent prognostic factor (training set: HR=4.422, p<0.001; TCGA-LIHC validation set: HR=2.561, p<0.001; ICGC-LIRI validation set: HR=3.931, p<0.001). Furthermore, a nomogram combining the model and AJCC stage was established and validated, showing increased OS predictive efficacy compared with the prognostic model (p=0.035) or AJCC stage (p<0.001).ConclusionsOur eight-gene prognostic model and the related nomogram represent as reliable prognostic tools for OS prediction in HCC patients.

Characterization of Immune Infiltration and Construction of a Prediction Model for Overall Survival in Melanoma Patients.

Reports indicate that the use of anti-programmed cell death-1 (PD-1) and death ligand-1 (PD-L1) monoclonal antibodies for the treatment of patients diagnosed with melanoma has demonstrated promising efficacy. Nonetheless, this therapy is limited by the resistance induced by the tumor microenvironment (TME). As such, understanding the complexity of the TME is vital in enhancing the efficiency of immunotherapy. This study used four different methods to estimate the infiltrating level of immune cells. Besides, we analyzed their infiltration pattern in primary and metastatic melanoma obtained from The Cancer Genome Atlas (TCGA) database. As a consequence, we discovered a significantly higher infiltration of immune cells in metastatic melanoma compared to primary tumor. Consensus clustering identified four clusters in melanoma with different immune infiltration and clusters with higher immune infiltration demonstrated a better overall survival. To elucidate the underlying mechanisms of immune cell infiltration, the four clusters were subdivided into two subtypes denoted as hot and cold tumors based on immune infiltration and predicted immune response. Enrichment analysis of differentially expressed genes (DEGs) revealed different transcriptome alterations in two types of tumors. Additionally, we found tyrosinase-related protein1 (TYRP1) was negatively correlated with CD8A expression. In vitro experiments showed that knockdown TYRP1 promoted the expression of HLA-A, B, and C. Eventually, we constructed a prediction model which was validated in our external cohort. Notably, this model also performed effectively in predicting the survival of patients under immunotherapy. In summary, this work provides a deeper understanding of the state of immune infiltration in melanoma and a prediction model that might guide the clinical treatment of patients with melanoma.

Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry.

Simple SummaryPrognostication of breast cancer patients is essential for risk communication and clinical decision-making. Many clinical tools for the survival prediction of breast cancer patients have been developed over the years. However, most of them were developed from Western countries. Studies have shown that these tools did not perform well in other ethnicities, such as Asian populations, including Thai. This study developed a new prediction model for survival predictions using modern statistical methods that allow a more accurate estimation of the baseline survival. The model was entitled the Individualized Prediction of Breast cancer Survival or the IPBS model. It contains twelve routinely available predictors that oncologists usually evaluate in daily practice. The survival information provided by the model was proven to be acceptably accurate and might be useful for physicians and patients, especially in Thailand or other Asian countries, to arrive at the most appropriate management plan.Prognostic models for breast cancer developed from Western countries performed less accurately in the Asian population. We aimed to develop a survival prediction model for overall survival (OS) and disease-free survival (DFS) for Thai patients with breast cancer. We conducted a prognostic model research using a multicenter hospital-based cancer clinical registry from the Network of National Cancer Institutes of Thailand. All women diagnosed with breast cancer who underwent surgery between 1 January 2010 and 31 December 2011 were included in the analysis. A flexible parametric survival model was used for developing the prognostic model for OS and DFS prediction. During the study period, 2021 patients were included. Of these, 1386 patients with 590 events were available for a complete-case analysis. The newly derived individualized prediction of breast cancer survival or the IPBS model consists of twelve routinely available predictors. The C-statistics from the OS and the DFS model were 0.72 and 0.70, respectively. The model showed good calibration for the prediction of five-year OS and DFS. The IPBS model provides good performance for the prediction of OS and PFS for breast cancer patients. A further external validation study is required before clinical implementation.

Rethinking prognostic factors in locally advanced or metastatic urothelial carcinoma in the immune checkpoint blockade era: a multicenter retrospective study

BackgroundFew studies have investigated the safety and efficacy of anti-PD-(L)1 antibodies in metastatic urothelial carcinoma (mUC) in daily clinical practice. Knowledge about the influence of baseline clinical and analytical factors on therapy outcomes is scarce.Patients and methodsWe conducted a multicenter retrospective study involving 119 previously treated or untreated mUC patients under anti-PD-(L)1 therapy in a real-world scenario. The objectives of this study were to confirm the safety and efficacy of anti-PD-(L)1 monotherapy and to identify pretreatment factors influencing therapy outcomes. In addition, an independent prognostic model for overall survival (OS) was developed and internally validated.ResultsMedian OS was 7.8 months [95% confidence interval (CI), 5.4-10.4], median progression-free survival (PFS) was 2.80 months (95% CI, 2.4-3.4), disease control rate (DCR) was 40% (95% CI, 31-49), and overall response rate (ORR) was 24% (95% CI, 15-31). Presence of peritoneal metastases was associated with poor OS [hazard ratio (HR) = 2.40, 95% CI, 1.08-5.33; P = 0.03]. Use of proton-pump inhibitors (PPI) was associated with poor OS (HR = 1.83, 95% CI, 1.11-3.02; P = 0.02) and PFS (HR = 1.94, 95% CI, 1.22-3.09; P = 0.005), and lower DCR (OR = 0.38, 95% CI, 0.17-0.89; P = 0.03) and ORR (OR = 0.18, 95% CI, 0.02-1.60; P = 0.002). The three risk category prognostic model developed included Eastern Cooperative Oncology Group performance status, PPI use, albumin level, presence of liver metastases, and presence of peritoneal metastases variables and was associated with higher risk of death (HR = 3.00, 95% CI, 1.97-4.56; P = 0.0001).ConclusionsThis study confirms anti-PD-(L)1 monotherapy as a safe and effective treatment option in daily clinical practice for mUC patients. It also describes the presence of peritoneal metastases as an independent prognostic factor for OS and underlines the association between PPI use and worse therapeutic outcomes. Finally, it proposes a new easy-to-use risk-assessment model for OS prediction.

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained to build a molecular prognostic score (mPS) system. Afterwards, we investigated the potential mechanisms underlying mPS by assessing gene set enrichment analysis, mutations, copy number variations (CNVs) and immune cell infiltration. A total of 275 prognosis-related genes were identified, which were also differentially expressed between ccRCC patients and healthy controls. We then constructed a universal mPS system that depends on the expression status of only 21 of these genes by applying AI-based algorithms. Then, the mPS were validated by another independent cohort and demonstrated to be applicable to ccRCC subsets. Furthermore, a nomogram comprising the mPS score and several independent variables was established and proved to effectively predict ccRCC patient prognosis. Finally, significant differences were identified regarding the pathways, mutated genes, CNVs and tumor-infiltrating immune cells among the subgroups of ccRCC stratified by the mPS system. The AI-based mPS system can provide critical prognostic prediction for ccRCC patients and may be useful to inform treatment and surveillance decisions before initial intervention.

SOURCE: Prediction Models for Overall Survival in Patients With Metastatic and Potentially Curable Esophageal and Gastric Cancer.

Personalized prediction of treatment outcomes can aid patients with cancer when deciding on treatment options. Existing prediction models for esophageal and gastric cancer, however, have mostly been developed for survival prediction after surgery (ie, when treatment has already been completed). Furthermore, prediction models for patients with metastatic cancer are scarce. The aim of this study was to develop prediction models of overall survival at diagnosis for patients with potentially curable and metastatic esophageal and gastric cancer (the SOURCE study). Data from 13,080 patients with esophageal or gastric cancer diagnosed in 2015 through 2018 were retrieved from the prospective Netherlands Cancer Registry. Four Cox proportional hazards regression models were created for patients with potentially curable and metastatic esophageal or gastric cancer. Predictors, including treatment type, were selected using the Akaike information criterion. The models were validated with temporal cross-validation on their C-index and calibration. The validated model's C-index was 0.78 for potentially curable gastric cancer and 0.80 for potentially curable esophageal cancer. For the metastatic models, the c-indices were 0.72 and 0.73 for esophageal and gastric cancer, respectively. The 95% confidence interval of the calibration intercepts and slopes contain the values 0 and 1, respectively. The SOURCE prediction models show fair to good c-indices and an overall good calibration. The models are the first in esophageal and gastric cancer to predict survival at diagnosis for a variety of treatments. Future research is needed to demonstrate their value for shared decision-making in clinical practice.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

A Prognostic Prediction Model of Transarterial Radioembolization in Hepatocellular Carcinoma: SNAP-HCC.

Prognosis prediction in patient with hepatocellular carcinoma (HCC) after transarterial radioembolization (TARE) remains difficult. The aim of this study was to develop a prognostic model to aid in the decision to use TARE. A total of 174 patients in Korea who underwent TARE for HCC as the initial treatment were included. We developed a prediction model for overall survival (OS) based on independent risk factors for OS and validated the model by bootstrap method. The median maximal size of the tumors was 8.2cm, the median number of tumors was 2, and the median albumin level was 4.0g/dL. Portal vein tumor thrombosis was found in 46.0% (Vp1-3 [39.7%] and Vp4 [6.3%]). Four independent risk factors associated with OS (maximal tumor size, tumor number, albumin, and portal vein tumor thrombosis) were used to develop the SNAP-HCC score. Bootstrap validation of the scoring index determined that the Harrell's c-index for OS was 0.756 (95% confidence interval: 0.729-0.783). Patients grouped based on their SNAP-HCC (scores 0-5) were well discriminated, with significant differences between the groups (all P < 0.05). Patients with SNAP-HCC < 3 showed significantly longer OS than patients with SNAP-HCC ≥ 3 (P < 0.001). The respective survival probabilities at years 1 and 3 were 0.81 and 0.73 in the low-risk (SNAP-HCC < 3) and 0.32 and 0.14 in the high-risk (SNAP-HCC ≥ 3) patients. The SNAP-HCC scoring system predicted the outcome of HCC patients undergoing TARE as an initial treatment. This model could be helpful for initial planning the treatment of HCC patients.