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124 results on '"Brinkhuis M."'

1. PL03.07 Invasion or No Invasion, That’s the Question. A Large Reproducibility Study in Pulmonary Adenocarcinoma, Supporting a Modified Classification

Author

Thunnissen, E., primary, Blaauwgeers, H., additional, Filipello, F., additional, Lissenberg-Witte, B., additional, Minami, Y., additional, Noguchi, M., additional, Le Quense, J., additional, Papotti, M.G., additional, Flieder, D., additional, Pelosi, G., additional, Sansano, I., additional, Berezowska, S., additional, Ryška, A., additional, Brcic, L., additional, Motoi, N., additional, Nakatani, Y., additional, Kuempers, C., additional, Hofman, P., additional, Hofman, V., additional, Grotnes Dale, V., additional, Rossi, G., additional, Ambrosi, F., additional, Matsubara, D., additional, Ishikawa, Y., additional, Weynand, B., additional, Calabrese, F., additional, Pezzuto, F., additional, Kern, I., additional, Nicholson, S., additional, Mutka, A.-L., additional, Dacic, S., additional, Beasley, M.B., additional, Arrigoni, G., additional, Timens, W., additional, Ooft, M., additional, Brinkhuis, M., additional, Bulkmans, N., additional, Britstra, R., additional, Vreuls, W., additional, Jones, K., additional, von der Thüsen, J., additional, Hager, H., additional, Perner, S., additional, Moore, D., additional, Leonte, D.G., additional, Al-Janabi, S., additional, Schønau, A., additional, Stenzinger, A., additional, and Kazdal, D., additional

Published
2023
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2. All that glitters is not gold: Four maturity stages of process discovery algorithms

Author

van der Werf, JMEM, Polyvyanyy, A, van Wensveen, BR, Brinkhuis, M, Reijers, HA, van der Werf, JMEM, Polyvyanyy, A, van Wensveen, BR, Brinkhuis, M, and Reijers, HA

Abstract

A process discovery algorithm aims to construct a process model that represents the real-world process stored in event data well; it is precise, generalizes the data correctly, and is simple. At the same time, it is reasonable to expect that better quality input event data should lead to constructed process models of better quality. However, existing process discovery algorithms omit the discussion of this relationship between the inputs and outputs and, as it turns out, often do not guarantee it. We demonstrate the latter claim using several quality measures for event data and discovered process models. Consequently, this paper requests for more rigor in the design of process discovery algorithms, including properties that relate the qualities of the inputs and outputs of these algorithms. We present four incremental maturity stages for process discovery algorithms, along with concrete guidelines for formulating relevant properties and experimental validation. We then use these stages to review several state of the art process discovery algorithms to confirm the need to reflect on how we perform algorithmic process discovery.

Published
2023

3. Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach

Author

Ferguson, R, Khosravi, H, Kovanović, V, Viberg, O, Aggarwal, A, Brinkhuis, M, Buckingham Shum, S, Chen, LK, Drachsler, H, Guerrero, VA, Hanses, M, Hayward, C, Hicks, B, Jivet, I, Kitto, K, Kizilcec, R, Lodge, JM, Manly, CA, Matz, RL, Meaney, MJ, Ochoa, X, Schuetze, BA, Spruit, M, Van Haastrecht, M, Van Leeuwen, A, Van Rijn, L, Tsai, Y-S, Weidlich, J, Williamson, K, Yan, VX, Ferguson, R, Khosravi, H, Kovanović, V, Viberg, O, Aggarwal, A, Brinkhuis, M, Buckingham Shum, S, Chen, LK, Drachsler, H, Guerrero, VA, Hanses, M, Hayward, C, Hicks, B, Jivet, I, Kitto, K, Kizilcec, R, Lodge, JM, Manly, CA, Matz, RL, Meaney, MJ, Ochoa, X, Schuetze, BA, Spruit, M, Van Haastrecht, M, Van Leeuwen, A, Van Rijn, L, Tsai, Y-S, Weidlich, J, Williamson, K, and Yan, VX

Abstract

NA

Published
2023

4. Prognostic refinement of NSMP high-risk endometrial cancers using oestrogen receptor immunohistochemistry

Author

Vermij, L., Jobsen, J.J., Leon-Castillo, A., Brinkhuis, M., Roothaan, S., Powell, M.E., Boer, S.M. de, Khaw, P., Mileshkin, L.R., Fyles, A., Leary, A., Genestie, C., Jnrgenliemk-Schulz, I.M., Crosbie, E.J., Mackay, H.J., Nijman, H.W., Nout, R.A., Smit, V.T.H.B.M., Creutzberg, C.L., Horeweg, N., Bosse, T., and TransPORTEC Consortium

Subjects
Cancer Research, Oncology, Manchester Cancer Research Centre, ResearchInstitutes_Networks_Beacons/mcrc
Abstract

Background Risk-assessment of endometrial cancer (EC) is based on clinicopathological factors and molecular subgroup. It is unclear whether adding hormone receptor expression, L1CAM expression or CTNNB1 status yields prognostic refinement. Methods Paraffin-embedded tumour samples of women with high-risk EC (HR-EC) from the PORTEC-3 trial (n = 424), and a Dutch prospective clinical cohort called MST (n = 256), were used. All cases were molecularly classified. Expression of L1CAM, ER and PR were analysed by whole-slide immunohistochemistry and CTNNB1 mutations were assessed with a next-generation sequencing. Kaplan–Meier method, log-rank tests and Cox’s proportional hazard models were used for survival analysis. Results In total, 648 HR-EC were included. No independent prognostic value of ER, PR, L1CAM, and CTNNB1 was found, while age, stage, and adjuvant chemotherapy had an independent impact on risk of recurrence. Subgroup-analysis showed that only in NSMP HR-EC, ER-positivity was independently associated with a reduced risk of recurrence (HR 0.33, 95%CI 0.15–0.75). Conclusions We confirmed the prognostic impact of the molecular classification, age, stage, and adjuvant CTRT in a large cohort of high-risk EC. ER-positivity is a strong favourable prognostic factor in NSMP HR-EC and identifies a homogeneous subgroup of NSMP tumours. Assessment of ER status in high-risk NSMP EC is feasible in clinical practice and could improve risk stratification and treatment.

Published
2023
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6. All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees

Author

LaRosa, M, Sadiq, S, Teniente, E, van der Werf, JM, Polyvyanyy, A, van Wensveen, B, Brinkhuis, M, Reijers, H, LaRosa, M, Sadiq, S, Teniente, E, van der Werf, JM, Polyvyanyy, A, van Wensveen, B, Brinkhuis, M, and Reijers, H

Abstract

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. intuitively, the better the quality of the input event data, the better the quality of the resulting discovered model should be. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

Published
2021

8. PurifyR: an R Package for highly automated reproducible variable extraction and standardization

Author

Omta, W., Heesbeen, R. van, Shen, I., Feelders, A., Brinkhuis, M., Egan, D., Spruit, M., Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, Software Technology for Learning and Teaching, Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, and Software Technology for Learning and Teaching

Subjects
Standardization, Computer science, Extraction (chemistry), knowledge discovery, R package, computer.software_genre, Data preparation, preprocessing standardization, data preparation, variable extraction, Variable (computer science), Knowledge extraction, Preprocessor, Data mining, computer
Abstract

Life science experiments that employ automated technologies, such as high-content screens, frequently produce large datasets that require substantial amounts of preprocessing before analysis can be carried out. Standardization of this preprocessing becomes impossible as the dataset size increases if there are manual steps involved. Virtually no standards for preprocessing currently exist and few user-friendly tools are available that allow the cleaning of data files in a simple and transparent manner while also allowing for reproducibility. We demonstrate in a publicly available R package, PurifyR, how preprocessing steps can be streamlined and automated. PurifyR supports multithreading and the standardization of large-matrix preprocessing. These steps provide transparent and reproducible preprocessing for matrix-oriented datasets. The PurifyR package is open source and can be downloaded from github.

Published
2020

9. Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

Author

Omta, W., van Heesbeen, R, Shen, Z., de Nobel, J., van der Velden, L., Medema, R., Siebes, A., Feelders, A., Brinkkemper, S., Klumperman, J., Spruit, M., Brinkhuis, M., Egan, D., Sub Software Production, Sub Natural Language Processing, Anatomie en fysiologie, Sub Algorithmic Data Analysis, Sub Organization and Information, Sub Softw.Techn. for Learning and Teach., Natural Language Processing, Algorithmic Data Analysis, Organization and Information, and Software Technology for Learning and Teaching

Subjects
classification, phenotypic profiles, supervised machine learning, artificial intelligence
Abstract

There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.

Published
2020

10. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.

Author

Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., and Pelders S.

Abstract

Background: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Method(s): We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Result(s): The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. Conclusion(s): Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.Copyright © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Published
2020

11. Prediction and clinical utility of a contralateral breast cancer risk model.

Author

Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., Van Deurzen C.H.M., Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., and Van Deurzen C.H.M.

Abstract

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Method(s): We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Result(s): In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was-0.13 (95% PI:-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusion(s): We developed a reasonab

Published
2020

12. Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

Author

Sub Software Production, Sub Natural Language Processing, Anatomie en fysiologie, Sub Algorithmic Data Analysis, Sub Organization and Information, Sub Softw.Techn. for Learning and Teach., Natural Language Processing, Algorithmic Data Analysis, Organization and Information, Software Technology for Learning and Teaching, Omta, W., van Heesbeen, R, Shen, Z., de Nobel, J., van der Velden, L., Medema, R., Siebes, A., Feelders, A., Brinkkemper, S., Klumperman, J., Spruit, M., Brinkhuis, M., Egan, D., Sub Software Production, Sub Natural Language Processing, Anatomie en fysiologie, Sub Algorithmic Data Analysis, Sub Organization and Information, Sub Softw.Techn. for Learning and Teach., Natural Language Processing, Algorithmic Data Analysis, Organization and Information, Software Technology for Learning and Teaching, Omta, W., van Heesbeen, R, Shen, Z., de Nobel, J., van der Velden, L., Medema, R., Siebes, A., Feelders, A., Brinkkemper, S., Klumperman, J., Spruit, M., Brinkhuis, M., and Egan, D.

Published
2020

14. PurifyR: an R Package for highly automated reproducible variable extraction and standardization

Author

Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, Software Technology for Learning and Teaching, Omta, W., Heesbeen, R. van, Shen, I., Feelders, A., Brinkhuis, M., Egan, D., Spruit, M., Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, Software Technology for Learning and Teaching, Omta, W., Heesbeen, R. van, Shen, I., Feelders, A., Brinkhuis, M., Egan, D., and Spruit, M.

Published
2020

15. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

Author

Giardiello, D, Hauptmann, M, Steyerberg, EW, Adank, MA, Akdeniz, D, Blom, JC, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Koppert, LB, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, van den Broek, AJ, van Deurzen, CHM, van Leeuwen, FE, van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, Schmidt, MK, Giardiello, D, Hauptmann, M, Steyerberg, EW, Adank, MA, Akdeniz, D, Blom, JC, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Koppert, LB, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, van den Broek, AJ, van Deurzen, CHM, van Leeuwen, FE, van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, and Schmidt, MK

Abstract

BACKGROUND: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Published
2020

16. Geohydrologische aanpak: nieuwe kijk op bestaande dijk

Author

Brinkhuis, M., Kalis, F., Hoogewoud, J., Brinkhuis, M., Kalis, F., and Hoogewoud, J.

Abstract

Waterschap Aa en Maas versterkt van Ravenstein tot Lith de dijk, gecombineerd met rivierverruimende maatregelen en gebieds- en natuurontwikkeling in de uiterwaarden. Parallel daaraan ontwikkelt zij een veelbelovende nieuwe aanpak voor piping. Maar daar is wel specialistisch onderzoek voor nodig. En het project mag niet vertragen.

Published
2020

19. Prediction and clinical utility of a contralateral breast cancer risk model

Author

Giardiello, D, Steyerberg, EW, Hauptmann, M, Adank, MA, Akdeniz, D, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, Van den Broek, AJ, Van Deurzen, CHM, Van Leeuwen, FE, Van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, Schmidt, MK, Giardiello, D, Steyerberg, EW, Hauptmann, M, Adank, MA, Akdeniz, D, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, Van den Broek, AJ, Van Deurzen, CHM, Van Leeuwen, FE, Van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, and Schmidt, MK

Abstract

BACKGROUND: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS: We developed a reasonably c

Published
2019

22. Potential Targets' Analysis Reveals Dual PI3K/mTOR Pathway Inhibition as a Promising Therapeutic Strategy for Uterine Leiomyosarcomas-an ENITEC Group Initiative

Author

Cuppens, T., Annibali, D., Coosemans, A., Trovik, J., Haar, N. Ter, Colas, E., Garcia-Jimenez, A., Vijver, K. van der, Kruitwagen, R.P., Brinkhuis, M., Zikan, M., Dundr, P., Huvila, J., Carpen, O., Haybaeck, J., Moinfar, F., Salvesen, H.B., Stukan, M., Mestdagh, C., Zweemer, R.P., Massuger, L.F.A.G., Mallmann, M.R., Wardelmann, E., Mints, M., Verbist, G., Thomas, D, Gomme, E., Hermans, E, Moerman, P., Bosse, T., Amant, F., Cuppens, T., Annibali, D., Coosemans, A., Trovik, J., Haar, N. Ter, Colas, E., Garcia-Jimenez, A., Vijver, K. van der, Kruitwagen, R.P., Brinkhuis, M., Zikan, M., Dundr, P., Huvila, J., Carpen, O., Haybaeck, J., Moinfar, F., Salvesen, H.B., Stukan, M., Mestdagh, C., Zweemer, R.P., Massuger, L.F.A.G., Mallmann, M.R., Wardelmann, E., Mints, M., Verbist, G., Thomas, D, Gomme, E., Hermans, E, Moerman, P., Bosse, T., and Amant, F.

Abstract

Contains fulltext : 170511.pdf (publisher's version ) (Closed access), Purpose: Uterine sarcomas are rare and heterogeneous tumors characterized by an aggressive clinical behavior. Their high rates of recurrence and mortality point to the urgent need for novel targeted therapies and alternative treatment strategies. However, no molecular prognostic or predictive biomarkers are available so far to guide choice and modality of treatment.Experimental Design: We investigated the expression of several druggable targets (phospho-S6S240 ribosomal protein, PTEN, PDGFR-alpha, ERBB2, and EGFR) in a large cohort of human uterine sarcoma samples (288), including leiomyosarcomas, low-grade and high-grade endometrial stromal sarcomas, undifferentiated uterine sarcomas, and adenosarcomas, together with 15 smooth muscle tumors of uncertain malignant potential (STUMP), 52 benign uterine stromal tumors, and 41 normal uterine tissues. The potential therapeutic value of the most promising target, p-S6S240, was tested in patient-derived xenograft (PDX) leiomyosarcoma models.Results: In uterine sarcomas and STUMPs, S6S240 phosphorylation (reflecting mTOR pathway activation) was associated with higher grade (P = 0.001) and recurrence (P = 0.019), as shown by logistic regression. In addition, p-S6S240 correlated with shorter progression-free survival (P = 0.034). Treatment with a dual PI3K/mTOR inhibitor significantly reduced tumor growth in 4 of 5 leiomyosarcoma PDX models (with tumor shrinkage in 2 models). Remarkably, the 4 responding models showed basal p-S6S240 expression, whereas the nonresponding model was scored as negative, suggesting a role for p-S6S240 in response prediction to PI3K/mTOR inhibition.Conclusions: Dual PI3K/mTOR inhibition represents an effective therapeutic strategy in uterine leiomyosarcoma, and p-S6S240 expression is a potential predictive biomarker for response to treatment. Clin Cancer Res; 23(5); 1274-85. (c)2017 AACR.

Published
2017

25. Rapid and reliable assessment of volume percentage of epithelium in borderline and invasive ovarian tumors

Author

Brugghe J, Jp, Baak, Gerrit Meijer, Pj, Diest, and Brinkhuis M

Subjects
Observer Variation, Ovarian Neoplasms, Time Factors, Photogrammetry, Image Interpretation, Computer-Assisted, Humans, Reproducibility of Results, Epithelial Cells, Female, Neoplasm Invasiveness
Abstract

To analyze factors determining intraobserver and interobserver reproducibility of stereology in borderline and invasive ovarian tumors.Fast and simple assessment of VPE was possible by using a highly automated interactive video overlay system suitable for application in a routine pathology laboratory. The point distance of a Weibel grid and the number of fields of vision per area of interest required to obtain good reproducibility were investigated. In addition, intraobserver and interobserver reproducibility was assessed, and the results of the improved technique were compared to those of the classical method.The experiments showed that intraobserver and interobserver variations in volume percentage of epithelium (VPE) assessments in a given case were caused mainly by high field-to-field variation and not so much by differences in the precision of assessment in a single field of vision. Therefore, many fields but only few points per field need to be measured to obtain, in a short time, a precise estimate of VPE in the measurement area of a tumor. From these results, an optimized protocol for VPE assessment was constructed. Using this protocol, nine observers independently assessed VPE in seven cases. Counting only one point in each of 100 systematically randomly sampled fields of vision (corresponding to a point distance of +/- 560 microns) yielded high intraobserver reproducibility (coefficient of variation [CV], 4%; R, .99; range, 0.98-1.00) and interobserver reproducibility (CV, 6%; R, .98; range, 0.97-1.00) in a short time. Assessment of one case took approximately three minutes, and the observers experienced the work as pleasant.VPE assessed with systematic random sampling, using a grid with one point per field of vision and counting 100 hits, yields an inexpensive, fast and highly reproducible measure of an important prognostic variable in ovarian tumors. This assessment can be performed easily in a routine pathology laboratory.

Published
1998

28. Minimum spanning tree analysis in advanced ovarian carcinoma. An investigation of sampling methods, reproducibility and correlation with histologic grade

Author

Brinkhuis M, Gerrit Meijer, Pj, Diest, Lt, Schuurmans, and Jp, Baak

Subjects
Ovarian Neoplasms, Double-Blind Method, Sample Size, Image Processing, Computer-Assisted, Discriminant Analysis, Humans, Reproducibility of Results, Cell Differentiation, Female, Sampling Studies
Abstract

To investigate sampling methods and reproducibility of minimum spanning tree (MST) variables in advanced ovarian cancer and their discriminative power for histologic grade.For the methodologic investigation, 30 cases of advanced ovarian cancer of the common epithelial types were used. These cases were equally distributed over the three histologic grades according to independent, "blind" assessments by three observers: well (n = 10), moderately (n = 10) and poorly (n = 10) differentiated. Additionally, the discriminative power of the MST variables for histologic grade was assessed in 64 cases (double-blind agreement upon grade by two observers). Measurements were performed on hematoxylin-eosin-stained tumor sections. In each field of vision the centers of gravity of tumor cell nuclei were interactively marked using a digitizing video overlay system, and an MST was computed. From each MST the number of points, total line length, average line length, minimum line length, maximum line length and percentage of points with one, two three and four neighbors were obtained. Optimal performance (coefficient of error5%) of the method was established when the MST was constructed in 12 systematically randomly selected fields of vision at a final magnification of 1,900x.Intraobserver and interobserver reproducibility showed good correlation coefficients for most MST variables. Univariate analysis revealed that total, average and minimum line length were significantly different between the three histologic grades. With a jacknifed stepwise discriminant analysis an overall correct classification of 75% for the three histologic grades was achieved in 64 cases, using the average line length, standard deviation of the line length and total line length.MST syntactic structure analysis offers an easy, fast and very reproducible technique that may be of help in objective grading of advanced ovarian cancers. Further studies are under way to investigate the prognostic value of MST analysis in advanced ovarian cancer.

Published
1997

29. Intratumor heterogeneity of morphometric and stereologic variables in primary ovarian tumors and their omental metastatic deposits

Author

Brinkhuis, M., Scheepstra, C., Buist, M. R., van Diest, P. J., Baak, J. P., and Other departments

Abstract

To compare quantitative pathologic variables assessed in primary ovarian tumors and metastatic tumor deposits in the omentum and compare their prognostic value. In 29 cases of advanced ovarian cancer the mean nuclear area (MNA), volume-weighted mean nuclear volume (vv), volume percentage epithelium (VPE) and mitotic activity index (MAI) were assessed in both the primary ovarian tumor and its metastatic deposits in the omentum. Differences were evaluated using the Wilcoxon rank sum test for paired observations, and coefficients of variation were calculated in each case over the values obtained from the tumor in the ovary and omentum. Intraobserver and interobserver reproducibility of MNA, VPE and MAI were all good to very good except for the interobserver reproducibility for vv, which was moderate. MNA and vv, correlated well, both in the primary ovarian tumor (r = .88) and omental metastasis (r = .87). No significant differences were found between the assessments of MNA, vv, and MAI in the primary ovarian tumor and its omental metastasis, whereas significant differences were found for VPE. However, in some patients the nuclei tended to be larger and the VPE lower in the omental metastasis than in the primary ovarian tumor. No important impact of the origin of tumor tissue was reflected in the prognostic value of the nuclear features. Patients were grouped prognostically differently for the assessment of MAI and VPE in the primary ovarian tumor and its omental metastasis. Quantitative pathologic variables for prognostic purposes are best assessed in the primary ovarian tumor. Measurements in the metastatic deposits may be helpful in understanding processes of metastasis in advanced ovarian cancer

Published
1997

31. Volume-weighted mean nuclear volume and nuclear area in advanced ovarian carcinoma. An investigation of sampling methods, sample size and reproducibility

Author

Brinkhuis, M, Meijer, G A, Beliën, J A, van Diest, P J, Baak, J P, Pathology, CCA - Cancer immunology, CCA - Biomarkers, and AII - Cancer immunology

Abstract

The influence of sampling issues on the reproducibility of volume-weighted mean nuclear volume (mean v) and mean nuclear area (MNA) assessments in patients with International Federation of Gynecology and Obstetrics stage III and IV ovarian carcinoma was evaluated. Ten cases representing the whole range of MNA values were selected from a population of 131 cases. The MNA and mean v of the same tumor cell nuclei were determined in one session by switching between the stereologic module and the morphometric module of the video overlay program used. For both MNA and mean v in one series of measurements, tumor nuclei were sampled from the whole tumor area and in a second series from the most poorly differentiated part (the measurement area) in each section, thus giving four series of measurements per case. For all four series, 500 nuclei were point sampled from approximately 100 systematically randomly selected fields of vision, using the automated scanning stage controlled by the morphometry program. These large samples, containing 500 nuclei for each case, were regarded as representative in each case. To investigate the susceptibility of MNA and mean v to variance at lower sampling levels (fields, nuclei), a nested analysis of variance was performed. Then the influence of sample size and sampling method was evaluated by drawing subsets from these 500 nuclei in each case in three different ways (cluster, systematic or random) with four different sample sizes (50, 100, 125, 250). It was shown that for MNA assessed in the measurement area, the variance between patients contributed the most to the total variance.(ABSTRACT TRUNCATED AT 250 WORDS)

Published
1995

32. Volume-weighted mean nuclear volume and nuclear area in advanced ovarian carcinoma. An investigation of sampling methods, sample size and reproducibility

Author

Brinkhuis M, Gerrit Meijer, Ja, Beliën, Pj, Diest, and Jp, Baak

Subjects
Cell Nucleus, Observer Variation, Ovarian Neoplasms, Carcinoma, Humans, Reproducibility of Results, Female, Sampling Studies
Abstract

The influence of sampling issues on the reproducibility of volume-weighted mean nuclear volume (mean v) and mean nuclear area (MNA) assessments in patients with International Federation of Gynecology and Obstetrics stage III and IV ovarian carcinoma was evaluated. Ten cases representing the whole range of MNA values were selected from a population of 131 cases. The MNA and mean v of the same tumor cell nuclei were determined in one session by switching between the stereologic module and the morphometric module of the video overlay program used. For both MNA and mean v in one series of measurements, tumor nuclei were sampled from the whole tumor area and in a second series from the most poorly differentiated part (the measurement area) in each section, thus giving four series of measurements per case. For all four series, 500 nuclei were point sampled from approximately 100 systematically randomly selected fields of vision, using the automated scanning stage controlled by the morphometry program. These large samples, containing 500 nuclei for each case, were regarded as representative in each case. To investigate the susceptibility of MNA and mean v to variance at lower sampling levels (fields, nuclei), a nested analysis of variance was performed. Then the influence of sample size and sampling method was evaluated by drawing subsets from these 500 nuclei in each case in three different ways (cluster, systematic or random) with four different sample sizes (50, 100, 125, 250). It was shown that for MNA assessed in the measurement area, the variance between patients contributed the most to the total variance.(ABSTRACT TRUNCATED AT 250 WORDS)

Published
1995

40. Effects of fulvestrant alone or combined with different steroids in human breast cancer cells in vitro.

Author

Jansen, G. H., Franke, H. R., Wolbers, F., Brinkhuis, M., and Vermes, I.

Subjects
BREAST cancer treatment, ADJUVANT treatment of cancer, HORMONE therapy for menopause, HORMONE therapy, STEROIDS, TAMOXIFEN, CLIMACTERIC
Abstract

Objectives Fulvestrant is an estrogen receptor (ER) antagonist that binds, blocks and degrades the estrogen receptor and is currently used in adjuvant treatment in postmenopausal women with ER-positive breast cancer as an alternative for tamoxifen. As an antagonist, it may induce or aggravate climacteric symptoms. In order to alleviate these symptoms, one could consider hormone therapy. The objective of this study was to analyze the effect of fulvestrant alone or in combination with different steroids in human breast cancer cells in vitro, and to demonstrate whether these steroids will compromise the efficacy of fulvestrant in ER-positive breast cancer cells. Methods We performed experiments in vitro with various hormone therapy preparations (estradiol (E2), dihydrodydrogesterone (DHD) and tibolone) at a concentration of 10-6 mol/l alone or combined with fulvestrant in different breast cancer cell lines, ER-positive and ER-negative. After an incubation of 144 h, proliferation and apoptosis were measured. The first was measured by quantification of the expression of cyclin D1 mRNA, the latter by the Nicoletti fragmentation assay. Results This in vitro study revealed clear differences in results when various hormone therapy preparations, alone or combined with fulvestrant, are added to ER-positive and ER-negative breast cancer cell lines. Conclusions Our study demonstrated that fulvestrant, an ER antagonist used in the treatment of ER-positive breast cancer, combined with E2 and DHD or in combination with tibolone, is not compromised in its efficacy in inducing apoptosis in ER-positive breast cancer cell lines in vitro. [ABSTRACT FROM AUTHOR]

Published
2008
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42. Influence of boundary effects on minimum spanning tree features. A computer simulation

Author

Gerrit Meijer, Pj, Diest, Jp, Baak, Brinkhuis M, and Beentjes M

Subjects
Image Processing, Computer-Assisted, Computer Simulation, Models, Biological
Abstract

To assess the influence of boundary effects on quantitative data derived from the minimum spanning tree (MST).In a computer simulation, 10 patterns of points (resembling populations of nuclei in tumor tissue) were generated, ranging from completely regular to very irregular. This allowed for assessing the influence of boundary effects in populations with different degrees of disorder. A stepwise reduction of the size of the sampling window enabled the analysis of the influence of boundary effects at different sample sizes (from 1,024 down to 9 points).Both the mean and coefficient of variation of all features remained rather constant with decreasing sample size down to a sample size of 64 points per MST.Based on this model, it is concluded that boundary effects have only a minor influence on the outcome of MST features when the sample size is larger than 64 points.

44. Photoacoustic imaging of breast tumor vascularization: A comparison with MRI and histopathology

Author

Heijblom, M., Piras, D., van den Engh, F.M., Klaase, Joost M., Brinkhuis, M., Steenbergen, Wiendelt, Manohar, Srirang, Ntziachristos, Vasilis, Lin, Charles P., Biomedical Photonic Imaging, and Faculty of Science and Technology

Subjects
METIS-298282, medicine.medical_specialty, Pathology, medicine.diagnostic_test, business.industry, Breast imaging, Ultrasound, Cancer, Magnetic resonance imaging, medicine.disease, Diffuse optical imaging, Vascularity, Breast cancer, IR-90010, medicine, Histopathology, Radiology, medicine.symptom, business
Abstract

Breast cancer is the most common form of cancer and the leading cause of cancer death among females. Early diagnosis improves the survival chances for the disease and that is why there is an ongoing search for improved methods for visualizing breast cancer. One of the hallmarks of breast cancer is the increase in tumor vascularization that is associated with angiogenesis: a crucial factor for survival of malignancies. Photoacoustic imaging can visualize the malignancyassociated increased hemoglobin concentration with optical contrast and ultrasound resolution, without the use of ionizing radiation or contrast agents and is therefore theoretically an ideal method for breast imaging. Previous clinical studies using the Twente Photoacoustic Mammoscope (PAM), which works in forward mode using a single wavelength (1064 nm), showed that malignancies can indeed be identified in the photoacoustic imaging volume as high contrast areas. However, the specific appearance of the malignancies led to questions about the contrast mechanism in relation to tumor vascularization. In this study, the photoacoustic lesion appearance obtained with an updated version of PAM is compared with the lesion appearance on Magnetic Resonance Imaging (MRI), both in general (19 patients) and on an individual basis (7 patients). Further, in 3 patients an extended histopathology protocol is being performed in which malignancies are stained for vascularity using an endothelial antibody: CD31. The correspondence between PAM and MRI and between PAM and histopathology makes it likely that the high photoacoustic contrast at 1064 nm is indeed largely the consequence of the increased tumor vascularization. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

45. Quantitative pathological variables as prognostic factors for overall survival in Danish patients with FIGO stage III ovarian cancer

Author

Brinkhuis, M., Lund, B., Meijer, G.A., and Baak, J.P.A.

Subjects
Ovarian cancer -- Prognosis, Pathology, Gynecological -- Analysis, Health, Analysis, Prognosis
Abstract

According to the authors' abstract of an article published in International Journal of Gynecological Cancer, 'Earlier studies have shown the strong prognostic value of the mitotic activity index (MAI), volume [...]

Published
1996

48. Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

Author

Bogaerts JM, Steenbeek MP, Bokhorst JM, van Bommel MH, Abete L, Addante F, Brinkhuis M, Chrzan A, Cordier F, Devouassoux-Shisheboran M, Fernández-Pérez J, Fischer A, Gilks CB, Guerriero A, Jaconi M, Kleijn TG, Kooreman L, Martin S, Milla J, Narducci N, Ntala C, Parkash V, de Pauw C, Rabban JT, Rijstenberg L, Rottscholl R, Staebler A, Van de Vijver K, Zannoni GF, van Zanten M, de Hullu JA, Simons M, and van der Laak JA

Subjects
Humans, Female, Cystadenocarcinoma, Serous diagnosis, Cystadenocarcinoma, Serous pathology, Reproducibility of Results, Observer Variation, Image Interpretation, Computer-Assisted, Deep Learning, Fallopian Tube Neoplasms pathology, Fallopian Tube Neoplasms diagnosis, Carcinoma in Situ pathology, Carcinoma in Situ diagnosis
Abstract

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Published
2024
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49. Evaluation of the recently established Dutch nationwide Archipelago of Ovarian Cancer Research biobank.

Author

Zelisse HS, van Gent MDJM, Mom CH, de Ridder S, Snijders MLH, Heeling M, Stoter M, Broeks A, Horlings HM, Lok CAR, Bosch SL, Piek JM, Bart J, Reyners AKL, Wisman GBA, Yigit R, Boere IA, Collée M, Groenendijk FH, Jansen MPHM, Roes EM, Hofhuis W, Hoogduin KJ, Alcalá LSM, Smedts HPM, Makkus ACF, Nieuwenhuyzen-de Boer GM, van Es N, Vencken PMLH, van Altena AM, Simons M, Hazelbag HM, Kagie MJ, Aliredjo R, Bonestroo TJJ, Bosse T, de Kroon CD, Brinkhuis M, Janssen MJ, Koster NC, Kruse AJ, Gerestein CG, Jonges TGN, Zweemer RP, Kooreman LFS, Lambrechts S, Ebisch IMW, de Kievit van der Heijden IM, Voorham QJ, van der Aa MA, Belien JAM, van de Vijver MJ, and Dijk F

Subjects
Humans, Female, Netherlands epidemiology, Middle Aged, Aged, Adult, Biomedical Research, Ovarian Neoplasms pathology, Biological Specimen Banks
Abstract

Fundamental and translational research in ovarian cancer aims to enhance understanding of disease mechanisms and improve treatment and survival outcomes. To support this, we established the Dutch multicenter, interdisciplinary Archipelago of Ovarian Cancer Research (AOCR) infrastructure, which includes a nationwide biobank. In this study, we share our experiences in establishing the infrastructure, offer guidance for similar initiatives, and evaluate the AOCR patient cohort. Key challenges included obtaining Data Protection Impact Assessment (DPIA) clearance, drafting the consortium agreement, and securing ethical approval from all hospitals. Over three years, 1093 patients were enrolled across 17 hospitals, resulting in the collection of 1339 tissue samples and 2280 blood samples. Of the 523 patients with currently available clinical and pathological data, 74 % (n = 387) had primary ovarian cancer. Among these patients, 73.4 % was diagnosed with high-grade serous ovarian carcinoma, and 80.9 % presented with advanced-stage disease. Surgery was performed on 93 % of patients with primary ovarian cancer, and chemotherapy was administered to 90.4 % of these patients. In conclusion, the AOCR biobank has established a robust foundation for future fundamental and translational ovarian cancer research. This manuscript provides valuable insights and guidance for developing future research infrastructures and biobanks, and contains detailed information about the AOCR patient cohort to date., Competing Interests: Declaration of competing interest The authors have no competing interests to declare that are relevant to the content of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2025
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50. QPOLE : A Quick, Simple, and Cheap Alternative for POLE Sequencing in Endometrial Cancer by Multiplex Genotyping Quantitative Polymerase Chain Reaction.

Author

Van den Heerik ASVM, Ter Haar NT, Vermij L, Jobsen JJ, Brinkhuis M, Roothaan SM, Leon-Castillo A, Ortoft G, Hogdall E, Hogdall C, Van Wezel T, Lutgens LCHW, Haverkort MAD, Khattra J, McAlpine JN, Creutzberg CL, Smit VTHBM, Gilks CB, Horeweg N, and Bosse T

Subjects
Female, Humans, Genotype, Poly-ADP-Ribose Binding Proteins genetics, Disease-Free Survival, Polymerase Chain Reaction, Endometrial Neoplasms diagnosis, Endometrial Neoplasms genetics, Endometrial Neoplasms pathology
Abstract

Purpose: Detection of 11 pathogenic variants in the POLE gene in endometrial cancer (EC) is critically important to identify women with a good prognosis and reduce overtreatment. Currently, POLE status is determined by DNA sequencing, which can be expensive, relatively time-consuming, and unavailable in hospitals without specialized equipment and personnel. This may hamper the implementation of POLE -testing in clinical practice. To overcome this, we developed and validated a rapid, low-cost POLE hotspot test by a quantitative polymerase chain reaction (qPCR) assay, QPOLE ., Materials and Methods: Primer and fluorescence-labeled 5'-nuclease probe sequences of the 11 established pathogenic POLE mutations were designed. Three assays, QPOLE -frequent for the most common mutations and QPOLE -rare-1 and QPOLE-rare-2 for the rare variants, were developed and optimized using DNA extracted from formalin-fixed paraffin-embedded tumor tissues. The simplicity of the design enables POLE status assessment within 4-6 hours after DNA isolation. An interlaboratory external validation study was performed to determine the practical feasibility of this assay., Results: Cutoffs for POLE wild-type, POLE -mutant, equivocal, and failed results were predefined on the basis of a subset of POLE mutants and POLE wild-types for the internal and external validation. For equivocal cases, additional DNA sequencing is recommended. Performance in 282 EC cases, of which 99 were POLE -mutated, demonstrated an overall accuracy of 98.6% (95% CI, 97.2 to 99.9), a sensitivity of 95.2% (95% CI, 90.7 to 99.8), and a specificity of 100%. After DNA sequencing of 8.8% equivocal cases, the final sensitivity and specificity were 96.0% (95% CI, 92.1 to 99.8) and 100%. External validation confirmed feasibility and accuracy., Conclusion: QPOLE is a qPCR assay that is a quick, simple, and reliable alternative for DNA sequencing. QPOLE detects all pathogenic variants in the exonuclease domain of the POLE gene. QPOLE will make low-cost POLE -testing available for all women with EC around the globe.

Published
2023
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