External multicentre validation of a nomogram predicting the risk of relapse in patients with borderline ovarian tumours.
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ABSTRACT:
SUBMITTER: Bendifallah S
PROVIDER: S-EPMC3844921 | biostudies-other | 2013 Nov
REPOSITORIES: biostudies-other
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Project description:BackgroundThe timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).MethodsThis study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).ResultsThe DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.ConclusionsThe US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
| S-EPMC11003110 | biostudies-literature
Project description:PurposePredicting lymph node metastasis (LNM) risk is crucial in determining further treatment strategies following endoscopic resection of T1 colorectal cancer (CRC). This study aimed to establish a new prediction model for the risk of LNM in T1 CRC patients.Materials and methodsThe development set included 833 patients with T1 CRC who had undergone endoscopic (n=154) or surgical (n=679) resection at the National Cancer Center. The validation set included 722 T1 CRC patients who had undergone endoscopic (n=249) or surgical (n=473) resection at Daehang Hospital. A logistic regression model was used to construct the prediction model. To assess the performance of prediction model, discrimination was evaluated using the receiver operating characteristic (ROC) curves with area under the ROC curve (AUC), and calibration was assessed using the Hosmer-Lemeshow (HL) goodness-of-fit test.ResultsFive independent risk factors were determined in the multivariable model, including vascular invasion, high-grade histology, submucosal invasion, budding, and background adenoma. In final prediction model, the performance of the model was good that the AUC was 0.812 (95% confidence interval [CI], 0.770 to 0.855) and the HL chi-squared test statistic was 1.266 (p=0.737). In external validation, the performance was still good that the AUC was 0.771 (95% CI, 0.708 to 0.834) and the p-value of the HL chi-squared test was 0.040. We constructed the nomogram with the final prediction model.ConclusionWe presented an externally validated new prediction model for LNM risk in T1 CRC patients, guiding decision making in determining whether additional surgery is required after endoscopic resection of T1 CRC.
| S-EPMC6790837 | biostudies-literature
Project description:To determine the oncological outcomes following fertility-sparing surgery (FSS) for the management of Borderline Ovarian Tumours (BOTs). A retrospective analysis of participants diagnosed with BOTs between January 2004 and December 2020 at the West London Gynaecological Oncology Centre was conducted. A total of 172 women were diagnosed; 52.3% (90/172) underwent FSS and 47.7% (82/172) non-FSS. The overall recurrence rate of disease was 16.9% (29/172), of which 79.3% (23/29) presented as the recurrence of serous or sero-mucinous BOTs and 20.7% (6/29) as low-grade serous carcinoma (LGSC). In the FSS group, the recurrence rate of BOTs was 25.6% (23/90) presenting a median 44.0 (interquartile range (IQR) 41.5) months, of which there were no episodes of recurrence presenting as LGSC reported. In the non-FSS group, all recurrences of disease presented as LGSC, with a rate of 7.7% (6/78), following a median of 47.5 months (IQR 47.8). A significant difference between the type of surgery performed (FSS v Non-FSS) and the association with recurrence of BOT was observed (Pearson Chi-Square: p = 0.000; x = 20.613). Twelve women underwent ultrasound-guided ovarian wedge resection (UGOWR) as a novel method of FSS. Recurrence of BOT was not significantly associated with the type of FSS performed (Pearson Chi- Square: x = 3.166, p = 0.379). Non-FSS is associated with negative oncological outcomes compared to FSS, as evidenced by the higher rate of recurrence of LGSC. This may be attributed to the indefinite long-term follow up with ultrasound surveillance all FSS women undergo, enabling earlier detection and treatment of recurrences.
| S-EPMC8946233 | biostudies-literature
Project description:BackgroundThe International Ovarian Tumour Analysis (IOTA) group have developed the ADNEX (The Assessment of Different NEoplasias in the adneXa) model to predict the risk that an ovarian mass is benign, borderline, stage I, stages II-IV or metastatic. We aimed to externally validate the ADNEX model in the hands of examiners with varied training and experience.MethodsThis was a multicentre cross-sectional cohort study for diagnostic accuracy. Patients were recruited from three cancer centres in Europe. Patients who underwent transvaginal ultrasonography and had a histological diagnosis of surgically removed tissue were included. The diagnostic performance of the ADNEX model with and without the use of CA125 as a predictor was calculated.ResultsData from 610 women were analysed. The overall prevalence of malignancy was 30%. The area under the receiver operator curve (AUC) for the ADNEX diagnostic performance to differentiate between benign and malignant masses was 0.937 (95% CI: 0.915-0.954) when CA125 was included, and 0.925 (95% CI: 0.902-0.943) when CA125 was excluded. The calibration plots suggest good correspondence between the total predicted risk of malignancy and the observed proportion of malignancies. The model showed good discrimination between the different subtypes.ConclusionsThe performance of the ADNEX model retains its performance on external validation in the hands of ultrasound examiners with varied training and experience.
| S-EPMC4997550 | biostudies-literature
Project description:To develop and externally validate a prostate health index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy in Chinese men with prostate-specific antigen 4-10 ng/mL and normal digital rectal examination (DRE). 347 men were recruited from two hospitals between 2012 and 2014 to develop a PHI-based nomogram to predict PCa. To validate these results, we used a separate cohort of 230 men recruited at another center between 2008 and 2013. Receiver operator curves (ROC) were used to assess the ability to predict PCa. A nomogram was derived from the multivariable logistic regression model and its accuracy was assessed by the area under the ROC (AUC). PHI achieved the highest AUC of 0.839 in the development cohort compared to the other predictors (p < 0.001). Including age and prostate volume, a PHI-based nomogram was constructed and rendered an AUC of 0.877 (95% CI 0.813-0.938). The AUC of the nomogram in the validation cohort was 0.786 (95% CI 0.678-0.894). In clinical effectiveness analyses, the PHI-based nomogram reduced unnecessary biopsies from 42.6% to 27% using a 5% threshold risk of PCa to avoid biopsy with no increase in the number of missed cases relative to conventional biopsy decision.
| S-EPMC4607975 | biostudies-literature
Project description:PurposeFor patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer.Methods199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90th percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported.ResultsLocal relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation).ConclusionIn these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.
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Project description:BackgroundApnea is one of the most life-threatening complications of bronchiolitis in children. This study aimed to determine early predictors of apnea in children hospitalized with bronchiolitis and develop a simple nomogram to identify patients at risk of apnea.MethodsThis retrospective, observational study included children hospitalized with bronchiolitis in two hospitals in China. Demographic and clinical characteristics, laboratory results, pathogens, and pulmonary iconography results were recorded. A training cohort of 759 patients (one hospital) was used to identify early predictors of apnea during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection. The nomogram was developed visually based on the variables selected by multivariable logistic regression analysis. Discrimination (concordance index, C-index), calibration, and decision curve analysis (DCA) were used to assess the model performance and clinical effectiveness.ResultsA total of 1,372 children hospitalized with bronchiolitis were retrospectively evaluated, 133 (9.69%) of whom had apnea. Apnea was observed in 80 of the 759 patients with bronchiolitis in the training cohort and 53 of the 613 patients in the external validation cohort. Underlying diseases, feeding difficulties, tachypnea, retractions and pulmonary atelectasis in the training cohort were independent risk factors for apnea and were assembled into the nomogram. The nomogram exhibited good discrimination with a C-index of 0.883 (95% CI: 0.839-0.927) and good calibration. The DCA showed that the nomogram was clinically useful in estimating the net benefit to patients.ConclusionWe developed a nomogram that is convenient to use and able to identify the individualized prediction of apnea risk in patients with bronchiolitis. These patients might benefit from early triage and more intensive monitoring.
| S-EPMC9627176 | biostudies-literature
Project description:The majority of borderline ovarian tumours (BOTs) behave in a benign fashion, but some may show aggressive behavior. The reason behind this has not been elucidated. The epidermal growth factor receptor (EGFR) is known to contribute to cell survival signals as well as metastatic potential of some tumours. EGFR expression and gene status have not been thoroughly investigated in BOTs as it has in ovarian carcinomas. In this study we explore protein expression as well as gene mutations and amplifications of EGFR in BOTs in comparison to a subset of other epithelial ovarian tumours. We studied 85 tumours, including 61 BOTs, 10 low grade serous carcinomas (LGSCs), 9 high grade serous carcinomas (HGSCs) and 5 benign epithelial tumours. EGFR protein expression was studied using immunohistochemistry. Mutations were investigated by Sanger sequencing exons 18-21 of the tyrosine kinase domain of EGFR. Cases with comparatively higher protein expression were examined for gene amplification by chromogenic in situ hybridization. We also studied the tumours for KRAS and BRAF mutations. Immunohistochemistry results revealed both cytoplasmic and nuclear EGFR expression with variable degrees between tumours. The level of nuclear localization was relatively higher in BOTs and LGSCs as compared to HGSCs or benign tumours. The degree of nuclear expression of BOTs showed no significant difference from that in LGSCs (mean ranks 36.48, 33.05, respectively, p=0.625), but was significantly higher than in HGSCs (mean ranks: 38.88, 12.61 respectively, p< 0.001) and benign tumours (mean ranks: 35.18, 13.00 respectively, p= 0.010). Cytoplasmic expression level was higher in LGSCs. No EGFR gene mutations or amplification were identified, yet different polymorphisms were detected. Five different types of point mutations in the KRAS gene and the V600E BRAF mutation were detected exclusively in BOTs and LGSCs. Our study reports for the first time nuclear localization of EGFR in BOTs. The nuclear localization similarities between BOTs and LGSCs and not HGSCs support the hypothesis suggesting evolution of LGSCs from BOTs. We also confirm that EGFR mutations and amplifications are not molecular events in the pathogenesis of BOTs.
| S-EPMC4891141 | biostudies-literature
Project description:This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.
| S-EPMC7275963 | biostudies-literature
Project description:BackgroundRenal trauma grading has a limited ability to distinguish patients who will need intervention after high-grade renal trauma (HGRT). A nomogram incorporating both clinical and radiologic factors has been previously developed to predict bleeding control interventions after HGRT. We aimed to externally validate this nomogram using multicenter data from level 1 trauma centers.MethodsWe gathered data from seven level 1 trauma centers. Patients with available initial computed tomography (CT) scans were included. Each CT scan was reviewed by two radiologists blinded to the intervention data. Nomogram variables included trauma mechanism, hypotension/shock, concomitant injuries, vascular contrast extravasation (VCE), pararenal hematoma extension, and hematoma rim distance (HRD). Mixed-effect logistic regression was used to assess the associations between the predictors and bleeding intervention. The prediction accuracy of the nomogram was assessed using the area under the receiver operating characteristic curve and its 95% confidence interval (CI).ResultsOverall, 569 HGRT patients were included for external validation. Injury mechanism was blunt in 89%. Using initial CT scans, 14% had VCE and median HRD was 1.7 (0.9-2.6) cm. Overall, 12% underwent bleeding control interventions including 34 angioembolizations and 24 nephrectomies. In the multivariable analysis, presence of VCE was associated with a threefold increase in the odds of bleeding interventions (odds ratio, 3.06; 95% CI, 1.44-6.50). Every centimeter increase in HRD was associated with 66% increase in odds of bleeding interventions. External validation of the model provided excellent discrimination in predicting bleeding interventions with an area under the curve of 0.88 (95% CI, 0.84-0.92).ConclusionOur results reinforce the importance of radiologic findings such as VCE and hematoma characteristics in predicting bleeding control interventions after renal trauma. The prediction accuracy of the proposed nomogram remains high using external data. These variables can help to better risk stratify high-grade renal injuries.Level of evidencePrognostic and epidemiological study, level III.
| S-EPMC8717860 | biostudies-literature