Introduction: Lymph node ratio (LNR) is an important prognostic factor of survival in patients with pancreatic ductal adenocarcinoma (PDAC). This study aimed to validate three LNR-based nomograms... Show moreIntroduction: Lymph node ratio (LNR) is an important prognostic factor of survival in patients with pancreatic ductal adenocarcinoma (PDAC). This study aimed to validate three LNR-based nomograms using an international cohort. Materials and methods: Consecutive PDAC patients who underwent upfront pancreatoduodenectomy from six centers (Europe/USA) were collected (2000-2017). Patients with metastases, R2 resection, missing LNR data, and who died within 90 postoperative days were excluded. The updated Amsterdam nomogram, the nomogram by Pu et al., and the nomogram by Li et al. were selected. For the validation, calibration, discrimination capacity, and clinical utility were assessed. Results: After exclusion of 176 patients, 10113 patients were included. Median overall survival (OS) of the cohort was 23 months (95% CI: 21-25). For the three nomograms, Kaplan-Meier curves showed significant OS diminution with increasing scores (p < 0.01). All nomograms showed good calibration (non-significant Hosmer-Lemeshow tests). For the Amsterdam nomogram, area under the ROC curve (AUROC) for 3-year OS was 0.64 and 0.67 for 5-year OS. Sensitivity and specificity for 3-year OS prediction were 65% and 59%. Regarding the nomogram by Pu et al., AUROC for 3- and 5-year OS were 0.66 and 0.70. Sensitivity and specificity for 3-year OS prediction were 68% and 53%. For the Li nomogram, AUROC for 3- and 5-year OS were 0.67 and 0.71, while sensitivity and specificity for 3-year OS prediction were 63% and 60%. Conclusion: The three nomograms were validated using an international cohort. Those nomograms can be used in clinical practice to evaluate survival after pancreatoduodenectomy for PDAC. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Hond, A. de; Raven, W.; Schinkelshoek, L.; Gaakeer, M.; Avest, E. ter; Sir, O.; ... ; Groot, B. de 2021
Objective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models... Show moreObjective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models predicting the hospitalization of ED patients and conventional regression techniques at three points in time after ED registration.Methods: We analyzed consecutive ED patients of three hospitals using the Netherlands Emergency Department Evaluation Database (NEED). We developed prediction models for hospitalization using an increasing number of data available at triage, similar to 30 min (including vital signs) and similar to 2 h (including laboratory tests) after ED registration, using ML (random forest, gradient boosted decision trees, deep neural networks) and multivariable logistic regression analysis (including spline transformations for continuous predictors). Demographics, urgency, presenting complaints, disease severity and proxies for comorbidity, and complexity were used as covariates. We compared the performance using the area under the ROC curve in independent validation sets from each hospital.Results: We included 172,104 ED patients of whom 66,782 (39 %) were hospitalized. The AUC of the multi-variable logistic regression model was 0.82 (0.78-0.86) at triage, 0.84 (0.81-0.86) at similar to 30 min and 0.83 (0.75-0.92) after similar to 2 h. The best performing ML model over time was the gradient boosted decision trees model with an AUC of 0.84 (0.77-0.88) at triage, 0.86 (0.82-0.89) at similar to 30 min and 0.86 (0.74-0.93) after similar to 2 h.Conclusions: Our study showed that machine learning models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admission. In comparison to the 30-min model, the 2-h model did not show a performance improvement. After further validation, these prediction models could support management decisions by real-time feedback to medical personal. Show less
There is a pending need for prognostic and predictive biomarkers in the treatment of patients with colorectal cancer.This thesis describes the prognostic and predictive application of the tumor... Show moreThere is a pending need for prognostic and predictive biomarkers in the treatment of patients with colorectal cancer.This thesis describes the prognostic and predictive application of the tumor-stroma ratio (TSR) in colorectal cancer, focusing on expanding current clinical-pathological standards and combining TSR with other diagnostic parameters. The TSR is a microscopy scoring method performed on hematoxylin-eosin stained tissue slides used for routine pathology assessment, and has proven to be a robust prognostic maker. Here, we investigate whether the TSR also exhibits predictive value with regard to adjuvant targeted therapy in stage II and III colon cancer. Moreover, exploring the value of collagen fiber organization in the intratumoral stroma, as well as combining this parameter with the TSR. Finally, expanding the application of the TSR with radiological diagnostics in rectal cancer. Assessing is there is a correlation between TSR and apparent diffusion coefficient values obtained from diagnostically performed MRI-DWI scans, in order to determine if there is potential with regards to neoadjuvant treatment choices or patient follow-up. Show less
This thesis describes the detailed method of scoring the tumor-stroma ratio and the different possibilities to use it in routine clinical diagnostics, for different types of cancer. It can be used... Show moreThis thesis describes the detailed method of scoring the tumor-stroma ratio and the different possibilities to use it in routine clinical diagnostics, for different types of cancer. It can be used not only for prognostic purposes, but it might also be useful for predicting the response on neo-adjuvant therapy. As it is an easy and cheap method, based on routine hematoxylin-eosin stained tissue slides used for daily pathology routine, it can be implemented in clinical diagnostics with little effort. Show less
