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Contains fulltext : 305684.pdf (Publisher’s version ) (Open Access)

01 juli 2023, Item does not contain fulltext, OBJECTIVES: To analyse variation in clinical management of cT1 renal cell carcinoma (RCC) in the Netherlands related to surgical hospital volume (HV). MATERIALS AND METHODS: Patients diagnosed with cT1 RCC during 2014-2020 were identified in the Netherlands Cancer Registry. Patient and tumour characteristics were retrieved. Hospitals performing kidney cancer surgery were categorised by annual HV as low (HV < 25), medium (HV = 25-49) and high (HV > 50). Trends over time in nephron-sparing strategies for cT1a and cT1b were evaluated. Patient, tumour and treatment characteristics of (partial) nephrectomies were compared by HV. Variation in applied treatment was studied by HV. RESULTS: Between 2014 and 2020, 10 964 patients were diagnosed with cT1 RCC. Over time, a clear increase in nephron-sparing management was observed. The majority of cT1a underwent a partial nephrectomy (PN), although less PNs were applied over time (from 48% in 2014 to 41% in 2020). Active surveillance (AS) was increasingly applied (from 18% to 32%). For cT1a, 85% received nephron-sparing management in all HV categories, either with AS, PN or focal therapy (FT). For T1b, radical nephrectomy (RN) remained the most common treatment (from 57% to 50%). Patients in high-volume hospitals underwent more often PN (35%) for T1b compared with medium HV (28%) and low HV (19%). CONCLUSION: HV is related to variation in the management of cT1 RCC in the Netherlands. The EAU guidelines have recommended PN as preferred treatment for cT1 RCC. In most patients with cT1a, nephron-sparing management was applied in all HV categories, although differences in applied strategy were found and PN was more frequently used in high HV. For T1b, high HV was associated with less appliance of RN, whereas PN was increasingly used. Therefore, closer guideline adherence was found in high-volume hospitals.

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Contains fulltext : 294868.pdf (Publisher’s version ) (Open Access), BACKGROUND: Ongoing research in the field of both localized, locally advanced and metastatic renal cell carcinoma has resulted in the availability of multiple treatment options. Hence, many questions are still unanswered and await further research. A nationwide collaborative registry allows to collect corresponding data. For this purpose, the Dutch PROspective Renal Cell Carcinoma cohort (PRO-RCC) has been founded, for the prospective collection of long-term clinical data, patient reported outcome measures (PROMs) and patient reported experience measures (PREMs). METHODS: PRO-RCC is designed as a multicenter cohort for all Dutch patients with renal cell carcinoma (RCC). Recruitment will start in the Netherlands in 2023. Importantly, participants may also consent to participation in a 'Trial within cohorts' studies (TwiCs). The TwiCs design provides a method to perform (randomized) interventional studies within the registry. The clinical data collection is embedded in the Netherlands Cancer Registry (NCR). Next to the standardly available data on RCC, additional clinical data will be collected. PROMS entail Health-Related Quality of Life (HRQoL), symptom monitoring with optional ecological momentary assessment (EMA) of pain and fatigue, and optional return to work- and/or nutrition questionnaires. PREMS entail satisfaction with care. Both PROMS and PREMS are collected through the PROFILES registry and are accessible for the patient and the treating physician. TRIAL REGISTRATION: Ethical board approval has been obtained (2021_218) and the study has been registered at ClinicalTrials.gov (NCT05326620). DISCUSSION: PRO-RCC is a nationwide long-term cohort for the collection of real-world clinical data, PROMS and PREMS. By facilitating an infrastructure for the collection of prospective data on RCC, PRO-RCC will contribute to observational research in a real-world study population and prove effectiveness in daily clinical practice. The infrastructure of this cohort also e

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-b