An external validation study of nomograms designed to predict isolated loco-regional and distant endometrial cancer recurrences: how applicable are they?
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ABSTRACT:
SUBMITTER: Bendifallah S
PROVIDER: S-EPMC3777006 | biostudies-other | 2013 Sep
REPOSITORIES: biostudies-other
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Project description:IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.
| S-EPMC9381832 | biostudies-literature
Project description:BackgroundAdjuvant therapy increases disease-free survival in endometrial cancer (EC), but has no impact on overall survival and negatively influences the quality of life. We investigated the discriminatory power of classical and immunological predictors of recurrence in a cohort of EC patients and confirmed the findings in an independent validation cohort.MethodsWe reanalysed the data from 355 EC patients and tested our findings in an independent validation cohort of 72 patients with EC. Predictors were selected and Harrell's C-index for concordance was used to determine discriminatory power for disease-free survival in the total group and stratified for histological subtype.ResultsPredictors for recurrence were FIGO stage, lymphovascular space invasion and numbers of cytotoxic and memory T-cells. For high risk cancer, cytotoxic or memory T-cells predicted recurrence as well as a combination of FIGO stage and lymphovascular space invasion (C-index 0.67 and 0.71 vs 0.70). Recurrence was best predicted when FIGO stage, lymphovascular space invasion and numbers of cytotoxic cells were used in combination (C-index 0.82). Findings were confirmed in the validation cohort.ConclusionsIn high-risk EC, clinicopathological or immunological variables can predict regional or distant recurrence with equal accuracy, but the use of these variables in combination is more powerful.
| S-EPMC4559831 | biostudies-literature
Project description:ObjectivePelvic and paraortic lymph nodal regions are frequent sites of relapse in women with endometrial cancer who have not undergone adjuvant external beam radiation. We investigated outcomes after definitive management of nodal relapses of endometrial cancer with intensity modulated radiation therapy (IMRT).MethodsBetween 2002-2012, 38 patients with endometrial cancer who had no prior external beam radiation were treated definitively using IMRT for regionally confined pelvic or paraortic nodal recurrences. Thirteen (34%) had chemotherapy prior to radiation, and 21 (55%) received concurrent chemotherapy. The nodal basins were typically treated to 45-50Gy, with a boost to the gross tumor to a median total of 64.7Gy (range 59-73Gy).ResultsThe median overall survival from date of recurrence was 46.1months and the 2-year survival was 71%. Patients who received concurrent chemotherapy had a significantly longer median survival (61.9months versus 28.7months, p=0.034). In-field failures were more frequent in patients who received chemotherapy prior to radiation, had a shorter recurrence-free interval, received a lower radiation dose, and had higher tumor grade. Three patients (8%) experienced grade 3-4 late gastrointestinal (GI) toxicity.ConclusionsLong-term survival can be achieved in women with nodal recurrences of endometrial cancer. The use of concurrent chemotherapy and dose escalation with IMRT as feasible may improve survival for women with isolated nodal recurrences of endometrial cancer.
| S-EPMC4915591 | biostudies-literature
Project description:Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We applied MetaMap to extract features from clinical narratives and also retrieved structured clinical data from EHR. Using these features, we trained a support vector machine model to identify distant recurrences in breast cancer patients. We trained the model using 1,396 double-annotated subjects and validated the model using 599 double-annotated subjects. In addition, we validated the model on a set of 4,904 single-annotated subjects as a generalization test. In the held-out test and generalization test, we obtained F-measure scores of 0.78 and 0.74, area under curve (AUC) scores of 0.95 and 0.93, respectively. To explore the representation learning utility of deep neural networks, we designed multiple convolutional neural networks and multilayer neural networks to identify distant recurrences. Using the same test set and generalizability test set, we obtained F-measure scores of 0.79 ± 0.02 and 0.74 ± 0.004, AUC scores of 0.95 ± 0.002 and 0.95 ± 0.01, respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
| S-EPMC7678240 | biostudies-literature
Project description:BackgroundDistant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.MethodsWeighted gene co-expression network analysis (WGCNA) was performed to discover co-expressed gene modules and hub genes associated with DM. Univariate and multivariate analyses were carried out to identify independent clinicopathological risk factors based on The Cancer Genome Atlas (TCGA) database. An integrated nomogram prediction model was established. Finally, real hub genes were validated using the GSE60542 database and various thyroid cell lines.ResultsThe midnightblue module was most significantly positively correlated with DM (R=0.56, P=9e-06) by as per WGCNA. DLX5 (AUC: 0.769), COX6B2 (AUC: 0.764), and LYPD1 (AUC: 0.760) were determined to be the real hub genes that play a crucial role in predicting DM. Meanwhile, univariate and multivariate analyses demonstrated that T-stage (OR, 15.03; 95% CI, 1.75-319.40; and P=0.024), histologic subtype (OR, 0.17; 95% CI, 0.03-0.92; and P=0.042) were the independent predictors of DM. Subsequently, a nomogram model was constructed based on gene signatures and independent clinical risk factors exhibited good performance. Additionally, the mRNA expressions of real hub genes in the GSE60542 dataset were consistent with TCGA.ConclusionsThe present study has provided a reliable model to predict DM in patients with DTC. This model is likely to serve as an individual risk assessment tool in therapeutic decision-making.
| S-EPMC8339873 | biostudies-literature
Project description:BackgroundClassification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.MethodsGenomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.ResultsReplication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for 'copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined 'high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.ConclusionsMolecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.
| S-EPMC4506381 | biostudies-literature
Project description:Acute graft-versus-host disease (GVHD) is still the major contributor to comorbidities and mortality after allogeneic hematopoietic stem cell transplantation. The use of plasmatic biomarkers to predict early outcomes has been advocated in the past decade. The purpose of this prospective noninterventional study was to test the ability of panels including 7 biomarkers (Elafin, HGF, IL2RA, IL8, REG3, ST2, and TNFRI), to predict day 28 (D28) complete response to steroid, D180 overall survival, and D180 nonrelapse mortality (NRM). Using previous algorithms developed by the Ann Arbor/MAGIC consortium, 204 patients with acute GVHD were prospectively included and biomarkers were measured at GVHD onset for all of them. Initial GVHD grade and bilirubin level were significantly associated with all those outcomes. After adjustment on clinical variables, biomarkers were associated with survival and NRM. In addition to clinical variables, biomarkers slightly improved the prediction of overall survival and NRM (concordance and net reclassification indexes). The potential benefit of adding biomarkers panel to clinical parameters was also investigated by decision curve analyses. The benefit of adding biomarkers to clinical parameters was however marginal for the D28 nonresponse and mortality endpoints.
| S-EPMC9631673 | biostudies-literature
Project description:Though several approaches for improving therapy have been carried out, the prognoses of TSCC patients still remains poor. Furthermore, biological markers, which are able to distinguish patients at high risk of recurrence has not been sufficiently explored. In this study, we identified and validated the 30-specific genes for predicting TSCC patiens prognosis. In addition, the gene expression profiling data was successfully converted to protein expression profiling data.
2017-07-31 | GSE78060 | GEO
Project description:BACKGROUND:The tumor immune microenvironment has clinicopathological significance in predicting prognosis and therapeutic efficacy. We aimed to develop an immune signature to predict distant metastasis in patients with nasopharyngeal carcinoma (NPC). METHODS:Using multiplexed quantitative fluorescence, we detected 17 immune biomarkers in a primary screening cohort of 54 NPC tissues presenting with/without distant metastasis following radical therapy. The LASSO (least absolute shrinkage and selection operator) logistic regression model used statistically significant survival markers in the training cohort (n=194) to build an immune signature. The prognostic and predictive accuracy of it was validated in an external independent group of 304 patients. RESULTS:Eight statistically significant markers were identified in the screening cohort. The immune signature consisting of four immune markers (PD-L1+ CD163+, CXCR5, CD117) in intratumor was adopted to classify patients into high and low risk in the training cohort and it showed a high level of reproducibility between different batches of samples (r=0.988 for intratumor; p<0.0001). High-risk patients had shorter distant metastasis-free survival (HR 5.608, 95%?CI 2.619 to 12.006; p<0.0001) and progression-free survival (HR 2.798, 95%?CI 1.498 to 5.266; p=0·001). The C-indexes which reflected the predictive capacity in training and validation cohort were 0.703 and 0.636, respectively. Low-risk patients benefited from induction chemotherapy plus concurrent chemoradiotherapy (IC+CCRT) (HR 0.355, 95%?CI 0.147 to 0.857; p=0·021), while high-risk patients did not (HR 1.329, 95% CI 0.543 to 3.253; p=0·533). To predict the individual risk of distant metastasis, nomograms with the integration of both immune signature and clinicopathological risk factors were developed. CONCLUSIONS:The immune signature provided a reliable estimate of distant metastasis risk in patients with NPC and might be applied to identify the cohort which benefit from IC+CCRT.
| S-EPMC7204817 | biostudies-literature
Project description:Objective:Peritoneal dissemination is difficult to diagnose by conventional imaging technologies. We aimed to construct a nomogram to predict peritoneal dissemination in gastric cancer (GC) patients. Methods:We retrospectively analyzed 1,112 GC patients in Sun Yat-sen University Cancer Center between 2001 and 2010 as the development set and 474 patients from The Sixth Affiliated Hospital, Sun Yat-sen University between 2010 and 2016 as the validation set. The clinicopathological variables associated with gastric cancer with peritoneal dissemination (GCPD) were analyzed. We used logistic regression analysis to identify independent risk factors for peritoneal dissemination. Then, we constructed a nomogram for the prediction of GCPD and defined its predictive value with a receiver operating characteristic (ROC) curve. External validation was performed to validate the applicability of the nomogram. Results:In total, 250 patients were histologically identified as having peritoneal dissemination. Logistic regression analysis demonstrated that age, sex, tumor location, tumor size, signet-ring cell carcinoma (SRCC), T stage, N stage and Borrmann classification IV (Borrmann IV) were independent risk factors for peritoneal dissemination. We constructed a nomogram consisting of these eight factors to predict GCPD and found an optimistic predictive capability, with a C-index of 0.791, an area under the curve (AUC) of 0.791, and a 95% confidence interval (95% CI) of 0.762-0.820. The results found in the external validation set were also promising. Conclusions:We constructed a highly sensitive nomogram that can assist clinicians in the early diagnosis of GCPD and serve as a reference for optimizing clinical management strategies.
| S-EPMC7219103 | biostudies-literature