| 1. |
- Bulten, W, et al.
(author)
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
- 2022
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In: Nature medicine. - : Springer Science and Business Media LLC. - 1546-170X .- 1078-8956. ; 28:1, s. 154-
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.
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| 2. |
- Geessink, Oscar G. F., et al.
(author)
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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
- 2019
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In: Cellular Oncology. - : SPRINGER. - 2211-3428 .- 2211-3436. ; 42:3, s. 331-341
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Journal article (peer-reviewed)abstract
- PurposeTumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images.MethodsHistological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a stroma-high or stroma-low group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times.ResultsWith stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio=2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio=2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis.ConclusionsThis work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.
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| 4. |
- Litjens, Carlijn H. C., et al.
(author)
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Physiologically-Based Pharmacokinetic Modelling to Predict the Pharmacokinetics and Pharmacodynamics of Linezolid in Adults and Children with Tuberculous Meningitis
- 2023
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In: Antibiotics. - : MDPI. - 2079-6382. ; 12:4
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Journal article (peer-reviewed)abstract
- Linezolid is used off-label for treatment of central nervous system infections. However, its pharmacokinetics and target attainment in cranial cerebrospinal fluid (CSF) in tuberculous meningitis patients is unknown. This study aimed to predict linezolid cranial CSF concentrations and assess attainment of pharmacodynamic (PD) thresholds (AUC:MIC of >119) in plasma and cranial CSF of adults and children with tuberculous meningitis. A physiologically based pharmacokinetic (PBPK) model was developed to predict linezolid cranial CSF profiles based on reported plasma concentrations. Simulated steady-state PK curves in plasma and cranial CSF after linezolid doses of 300 mg BID, 600 mg BID, and 1200 mg QD in adults resulted in geometric mean AUC:MIC ratios in plasma of 118, 281, and 262 and mean cranial CSF AUC:MIC ratios of 74, 181, and 166, respectively. In children using similar to 10 mg/kg BID linezolid, AUC:MIC values at steady-state in plasma and cranial CSF were 202 and 135, respectively. Our model predicts that 1200 mg per day in adults, either 600 mg BID or 1200 mg QD, results in reasonable (87%) target attainment in cranial CSF. Target attainment in our simulated paediatric population was moderate (56% in cranial CSF). Our PBPK model can support linezolid dose optimization efforts by simulating target attainment close to the site of TBM disease.
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| 5. |
- Litjens, Carlijn H. C., et al.
(author)
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Prediction of Moxifloxacin Concentrations in Tuberculosis Patient Populations by Physiologically Based Pharmacokinetic Modeling
- 2022
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In: Journal of clinical pharmacology. - : John Wiley & Sons. - 0091-2700 .- 1552-4604. ; 62:3, s. 385-396
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Journal article (peer-reviewed)abstract
- Moxifloxacin has an important role in the treatment of tuberculosis (TB). Unfortunately, coadministration with the cornerstone TB drug rifampicin results in suboptimal plasma exposure. We aimed to gain insight into the moxifloxacin pharmacokinetics and the interaction with rifampicin. Moreover, we provided a mechanistic framework to understand moxifloxacin pharmacokinetics. We developed a physiologically based pharmacokinetic model in Simcyp version 19, with available and newly generated in vitro and in vivo data, to estimate pharmacokinetic parameters of moxifloxacin alone and when administered with rifampicin. By combining these strategies, we illustrate that the role of P-glycoprotein in moxifloxacin transport is limited and implicate MRP2 as transporter of moxifloxacin-glucuronide followed by rapid hydrolysis in the gut. Simulations of multiple dose area under the plasma concentration-time curve (AUC) of moxifloxacin (400 mg once daily) with and without rifampicin (600 mg once daily) were in accordance with clinically observed data (predicted/observed [P/O] ratio of 0.87 and 0.80, respectively). Importantly, increasing the moxifloxacin dose to 600 mg restored the plasma exposure both in actual patients with TB as well as in our simulations. Furthermore, we extrapolated the single dose model to pediatric populations (P/O AUC ratios, 1.04-1.52) and the multiple dose model to children with TB (P/O AUC ratio, 1.51). In conclusion, our combined approach resulted in new insights into moxifloxacin pharmacokinetics and accurate simulations of moxifloxacin exposure with and without rifampicin. Finally, various knowledge gaps were identified, which may be considered as avenues for further physiologically based pharmacokinetic refinement.
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