A Nomogram Predicting Progression Free Survival in Patients with Gastrointestinal Stromal Tumor Receiving Sunitinib: Incorporating Pre-Treatment and Post-Treatment Parameters.
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SUBMITTER: Chang YR
PROVIDER: S-EPMC8197516 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the gastrointestinal tract. In advanced setting and after progression to imatinib, the multi-targeted receptor tyrosine kinase inhibitor sunitinib has clearly demonstrated a clinical benefit in terms of response rate and progression-free survival with an acceptable toxicity profile. The recommended schedule for sunitinib administration is 50 mg per day 4 weeks ON and 2 weeks OFF; however, potential alternative schedules are also reviewed in the present article. Several biomarkers have been explored to better select candidates for sunitinib therapy, such as the value of early changes in standardized uptake value assessed by positron emission tomography with 18F-fluorodeoxyglucose, circulating biomarkers, clinical biomarkers such as the appearance of arterial hypertension during treatment that correlates with better outcomes, and the GIST genotype. GISTs with KIT mutations at exon 9 and the so-called wild-type GISTs seem to better respond to sunitinib. Nonetheless, further investigation is required to confirm these findings as well as to understand the mechanisms of sunitinib resistance such as the development of new KIT mutations or conformational changes in KIT receptor.
| S-EPMC5171199 | biostudies-literature
Project description:BackgroundLiver metastasis (LIM) of gastrointestinal stromal tumor (GIST) is associated with poor prognosis. The present study aimed at developing and validating nomogram to predict LIM in patients with GIST, thus helping clinical diagnosis and treatment.MethodsThe data of GIST patients derived from Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016, which were then screened by univariate and multivariate logistic regression for the construction of LIM nomogram. The model discrimination of LIM nomogram was evaluated by concordance index (C-index) and calibration plots, while the predictive accuracy and clinical values were measured by decision curve analysis (DCA) and clinical impact plot. Furthermore, we validated predictive nomogram in the internal testing set.ResultsA total of 3797 patients were enrolled and divided randomly into training and validating groups in a 3-to-1 ratio. After logistic regression, the significant variables were sex, tumor location, tumor size, N stage and mitotic rate. The calibration curves showed the perfect agreement between nomogram predictions and actual observations, while the DCA and clinical impact plot showed the clinical utility of LIM nomogram. C-index of the nomogram was 0.812. What's more, receiver operating characteristic curves (ROC) also showed good discrimination and calibration in the training set (AUC = 0.794, 95% CI 0.778-0.808) and the testing set (AUC = 0.775, 95% CI 0.748-0.802).ConclusionThe nomogram for patients with GIST can effectively predict the individualized risk of liver metastasis and provide insightful information to clinicians to optimize therapeutic regimens.
| S-EPMC7689971 | biostudies-literature
Project description:Gastrointestinal stromal tumours (GIST) are rarely encountered mesenchymal tumours of the gastrointestinal tract (1.5 people per 100,000/year) that are even more rarely seen in paediatric patients (1-2% of all cases). The standard treatment for advanced adult GIST is imatinib with sunitinib as a second-line option. Although the efficacy and tolerability of sunitinib in adults with GIST has been established, little is known of the profile of sunitinib in paediatric/young adult patients with GIST given the rarity of this disease.Paediatric/young adult patients aged up to 21 years with diagnosis of GIST who were treated with sunitinib were identified from retrospective records from three centres in Europe and the US. Most patients commenced sunitinib in a 6-week cycle, however, dosing could be reduced, delayed, changed to (or initiated with) a continuous schedule. Objective response (Response Evaluation Criteria In Solid Tumours [RECIST]) and adverse events were recorded.We identified 9 paediatric/young adult patients (aged 11-21 years) with GIST who were treated with sunitinib de novo (n = 1) or following failure of imatinib (n = 8). Progressive disease was previously documented for all patients including 7 patients during imatinib therapy. Baseline patient and tumour profile characteristics showed a distinct profile (notably all were wild-type KIT/PDGFR) compared to that established for adults. Sunitinib treatment was associated with a best response of stable disease for 7 patients, with disease stabilisation lasting from 1 month to >73 months and a median progression free survival time of 15 months. There was some evidence of better disease control for sunitinib when compared to prior imatinib. Most adverse events with sunitinib were manageable and all were consistent with the known profile of the agent.The ability to draw firm conclusions from this case series is limited by the small number of patients and the use of retrospective data which is largely reflective of the rarity of this condition. However, our findings provide initial evidence of clinical benefit and a generally manageable toxicity profile for sunitinib when administered to paediatric/young adult patients with GIST, most of whom had documented progressive disease during prior imatinib treatment.
| S-EPMC5674814 | biostudies-literature
Project description:PurposeTo evaluate changes in circulating levels of soluble KIT (sKIT) extracellular domain as a potential biomarker for clinical outcome in gastrointestinal stromal tumor patients treated with the multitargeted tyrosine kinase inhibitor sunitinib following imatinib failure in a previously reported phase III study.Experimental designPatients received sunitinib 50 mg/d (n = 243) or placebo (n = 118) daily in 6-week cycles (4 weeks on, 2 weeks off treatment). Plasma sKIT levels were sampled every 2 weeks in cycle 1 and on days 1 and 28 of subsequent cycles; analyzed by ELISA; and evaluated using Prentice criteria, Cox proportional hazards models, and proportion of treatment effect (PTE) analysis.ResultsFrom 4 weeks on treatment and onward, significant differences were shown between treatment groups (P < 0.0001) in sKIT level changes from baseline (median levels decreased with sunitinib and increased with placebo). Decreases in sKIT levels were a significant predictor of longer time to tumor progression (TTP). Patients with reduced levels at the end of cycle 2 had a median TTP of 34.3 weeks versus 16.0 weeks for patients with increased levels [hazard ratio, 0.71; 95% confidence interval (95% CI), 0.61-0.83; P < 0.0001], and changes in sKIT levels replaced treatment as a stronger predictor of TTP (PTE, 0.80; 95% CI, 0.34-3.70), showing even greater surrogacy on cycle 3 day 1 (PTE, 0.98; 95% CI, 0.39-3.40).ConclusionsThe results suggest that circulating plasma sKIT levels seem to function as a surrogate marker for TTP in gastrointestinal stromal tumor patients. Additional studies are warranted to confirm and expand these findings.
| S-EPMC3500590 | biostudies-literature
Project description:BACKGROUND AND AIM:To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHOD:A total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki-67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. RESULTS:The radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632-0.801], 0.765 (95% CI 0.683-0.847), and 0.754 (95% CI 0.666-0.842) in the prediction of high Ki-67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726-0.876), 0.828 (95% CI 0.681-0.974), and 0.784 (95% CI 0.701-0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. CONCLUSIONS:The radiomic signature from CE-CT was significantly associated with Ki-67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki-67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.
| S-EPMC6994569 | biostudies-literature
Project description:PurposeThe starting dose of sunitinib in children with gastrointestinal stromal tumors (GIST) was extrapolated based on data in adults with GIST or solid tumors and children with solid tumors.MethodsIntegrated population pharmacokinetics (PK), PK/pharmacodynamics (PD), and exposure-response analyses using nonlinear mixed-effects modeling approaches were performed to extrapolate PK and PD of sunitinib in children with GIST at projected dose(s) with plasma drug exposures comparable to 50-mg/day in adults with GIST. The analysis datasets included PK/PD data in adults with GIST and adults and children with solid tumors. The effect of covariates on PK and safety/efficacy endpoints were explored.ResultsTwo-compartment models with lag time were successfully used to describe the PK of sunitinib and its active metabolite SU012662. PK/PD models were successfully built to describe key continuous safety and efficacy endpoints. The effect of age on sunitinib apparent clearance (CL/F) and body surface area on SU012662 CL/F was statistically significant (P ≤ 0.001): children who were younger or of smaller body size had lower CL/F; however, age and body size did not appear to negatively affect safety or efficacy response to plasma drug exposure.ConclusionBased on PK, safety, and efficacy trial simulations, a sunitinib starting dose of ~ 25 mg/m2/day was predicted to provide comparable plasma drug exposures in children with GIST as in adults with GIST treated with 50 mg/day. However, in the absence of a tumor type effect of sunitinib on CL/F in children, the projected equivalent dose for this population would be ~ 20 mg/m2/day.
| S-EPMC8026416 | biostudies-literature
Project description:ImportanceAnticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making.ObjectiveTo compare the performance of 3 machine learning approaches with the commonly used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related GIB.Design, setting, and participantsThis retrospective cross-sectional study used data from the OptumLabs Data Warehouse, which contains medical and pharmacy claims on privately insured patients and Medicare Advantage enrollees in the US. The study cohort included patients 18 years or older with a history of atrial fibrillation, ischemic heart disease, or venous thromboembolism who were prescribed oral anticoagulant and/or thienopyridine antiplatelet agents between January 1, 2016, and December 31, 2019.ExposuresA cohort of patients prescribed oral anticoagulant and thienopyridine antiplatelet agents was divided into development and validation cohorts based on date of index prescription. The development cohort was used to train 3 machine learning models to predict GIB at 6 and 12 months: regularized Cox proportional hazards regression (RegCox), random survival forests (RSF), and extreme gradient boosting (XGBoost).Main outcomes and measuresThe performance of the models for predicting GIB in the validation cohort, evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, and prediction density plots. Relative importance scores were used to identify the variables that were most influential in the top-performing machine learning model.ResultsIn the entire study cohort of 306 463 patients, 166 177 (54.2%) were male, 193 648 (63.2%) were White, the mean (SD) age was 69.0 (12.6) years, and 12 322 (4.0%) had experienced a GIB. In the validation data set, the HAS-BLED model had an AUC of 0.60 for predicting GIB at 6 months and 0.59 at 12 months. The RegCox model performed the best in the validation set, with an AUC of 0.67 at 6 months and 0.66 at 12 months. XGBoost was similar, with AUCs of 0.67 at 6 months and 0.66 at 12 months, whereas for RSF, AUCs were 0.62 at 6 months and 0.60 at 12 months. The variables with the highest importance scores in the RegCox model were prior GI bleed (importance score, 0.72); atrial fibrillation, ischemic heart disease, and venous thromboembolism combined (importance score, 0.38); and use of gastroprotective agents (importance score, 0.32).Conclusions and relevanceIn this cross-sectional study, the machine learning models examined showed similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance.
| S-EPMC8140376 | biostudies-literature
Project description:BackgroundThe objectives of this study were to provide sunitinib to patients with gastrointestinal stromal tumor (GIST) who were otherwise unable to obtain it and to collect broad safety and efficacy data from a large population of patients with advanced GIST after imatinib failure. (ClinicalTrials.gov identifier NCT00094029).MethodsImatinib-resistant/intolerant patients with advanced GIST received sunitinib on an initial dosing schedule of 50 mg daily in 6-week cycles (4 weeks on treatment, 2 weeks off treatment). Tumor assessment frequency was according to local practice, and response was assessed by investigators according to Response Evaluation Criteria in Solid Tumors version 1.0. Overall survival (OS) and safety were assessed regularly. Post hoc analyses evaluated different patterns of treatment management.ResultsAt final data cutoff, 1124 patients comprised the intent-to-treat population, and 15% of these patients had a baseline Eastern Cooperative Oncology Group performance status ≥2. The median treatment duration was 7.0 months. The median time to tumor progression was 8.3 months (95% confidence interval [CI], 8.0-9.4 months), the median OS was 16.6 months (95% CI, 14.9-18.0 months), and 36% of patients were alive at the time of analysis. Patients for whom the initial dosing schedule was modified exhibited longer median OS (23.5 months) than those who were treated strictly according to the initial dosing schedule (11.1 months). The most common treatment-related grade 3 and 4 adverse events were hand-foot syndrome (11%), fatigue (9%), neutropenia (8%), hypertension (7%), and thrombocytopenia (6%). Treatment-related adverse events associated with cardiac function (eg, congestive heart failure and myocardial infarction) were reported at frequencies of ≤1% each.ConclusionsThis treatment-use study confirms the long-term safety and efficacy of sunitinib in a large international population of patients with advanced GIST after imatinib failure.
| S-EPMC4442000 | biostudies-literature
Project description:BackgroundGastrointestinal stromal tumor (GIST) is a common mesenchymal tumor of the gastrointestinal system. They originate from the interstitial cells of Cajal located within the muscle layer and are characterized by over-expression of the tyrosine kinase receptor KIT.MethodsData from the Surveillance Epidemiology, and End Results (SEER) database of 1,213 patients diagnosed with GIST between 2010 and 2019 were dichotomized into a modeling set and a validation set at a 2:1 ratio. For the modeling set, both univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram was then constructed based on these determinants. Model efficacy was tested using receiver operating characteristic (ROC) curves, calibration curves, clinical decision curves, and risk stratification analysis in both subsets.ResultsIdentified prognostic determinants included age, sex, pathological differentiation level, tumor-node-metastasis (TNM) stage, surgical intervention, radiotherapy, and marital status. The constructed nomogram showed area under the ROC curve (AUC) values of 0.822, 0.793, and 0.779 for 1-, 3-, and 5-year overall survival (OS) in the modeling set, respectively, while in the validation set, the values were 0.796, 0.823, and 0.806, respectively. Calibration plots from both sets confirmed the concordance between predicted and observed survival. Decision curve analysis (DCA) indicated significant clinical utility for the nomogram. Risk stratification of the patient data revealed distinct survival differences between high-risk and low-risk cohorts in both sets (P<0.001).ConclusionsA novel and potent nomogram for the prognosis of GIST has been introduced. This model's precision offers crucial insights for clinical decisions, yet further external validation remains essential.
| S-EPMC10932676 | biostudies-literature
Project description:Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are the most prevalent histologic types of primary liver cancer (PLC). Although ICC and HCC share similar risk factors and clinical manifestations, ICC usually bears poorer prognosis than HCC. Confidently discriminating ICC and HCC before surgery is beneficial to both treatment and prognosis. Given the lack of effective differential diagnosis biomarkers and methods, construction of models based on available clinicopathological characteristics is in need. Nomograms present a simple and efficient way to make a discrimination. A total of 2894 patients who underwent surgery for PLC were collected. Of these, 1614 patients formed the training cohort for nomogram construction, and thereafter, 1280 patients formed the validation cohort to confirm the model's performance. Histopathologically confirmed ICC was diagnosed in 401 (24.8%) and 296 (23.1%) patients in these two cohorts, respectively. A nomogram integrating six easily obtained variables (Gender, Hepatitis B surface antigen, Aspartate aminotransferase, Alpha-fetoprotein, Carcinoembryonic antigen, Carbohydrate antigen 19-9) is proposed in accordance with Akaike's Information Criterion (AIC). A score of 15 was determined as the cut-off value, and the corresponding discrimination efficacy was sufficient. Additionally, patients who scored higher than 15 suffered poorer prognosis than those with lower scores, regardless of the subtype of PLC. A nomogram for clinical discrimination of ICC and HCC has been established, where a higher score indicates ICC and poor prognosis. Further application of this nomogram in multicenter investigations may confirm the practicality of this tool for future clinical use.
| S-EPMC5852370 | biostudies-literature