92 results on '"Huiskens, J."'
Search Results
2. Regional and inter-hospital differences in the utilisation of liver surgery for patients with synchronous colorectal liver metastases in the Netherlands
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Dejong, C.H.C., Grunhagen, D., van Gulik, T.M., de Jong, K.P., Kazemier, G., Molenaar, I.Q., Ruers, T.M., 't Lam-Boer, J., van der Stok, E.P., Huiskens, J., Verhoeven, R.H.A., Punt, C.J.A., Elferink, M.A.G., de Wilt, J.H., and Verhoef, C.
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- 2017
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3. Interobserver Variability in Morphologic Tumor Response Assessment Following Systemic Therapy in Patients with Initially Unresectable Colorectal Liver Metastases
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Wesdorp, N.J., primary, Kemna, R., additional, Waesberghe, J.-H.T. van, additional, Nota, I.M., additional, Struik, F., additional, Abdennabi, I. Oulad, additional, Phoa, S.S., additional, van Dieren, S., additional, Swijnenburg, R.-J., additional, Punt, C.J., additional, Huiskens, J., additional, Stoker, J., additional, and Kazemier, G., additional
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- 2022
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4. Development and Validation of Auto-segmentation Deep Learning Models for Automatic Total Tumor Volume Assessment in Patients with Initially Unresectable Colorectal Liver Metastases
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Wesdorp, N.J., primary, Zeeuw, J.M., additional, Postma, S.C., additional, Roor, J., additional, Vadakkumpadan, F., additional, Ambrozic, C., additional, Waesberghe, J.-H.T. van, additional, Swijnenburg, R.-J., additional, Punt, C.J., additional, Huiskens, J., additional, and Kazemier, G., additional
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- 2022
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5. Machine learning-based auto-segmentation of histological residual tumor in resected pancreatic cancer after neoadjuvant therapy
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Janssen, B., primary, Theijse, R., additional, van Roessel, S., additional, de Ruiter, R., additional, Berkel, A., additional, Huiskens, J., additional, Busch, O., additional, Wilmink, J., additional, Kazemier, G., additional, Valkema, P., additional, Farina, A., additional, Verheij, J., additional, Besselink, M., additional, and de Boer, O., additional
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- 2021
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6. Total tumor volume response versus RECIST upon systemic treatment in patients with initially unresectable colorectal liver metastases
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Wesdorp, N.J., primary, Bolhuis, K., additional, Roor, J., additional, van Waesberghe, J.H.T.M., additional, van Dieren, S., additional, Swijnenburg, R.J., additional, Punt, C.J.A., additional, Huiskens, J., additional, and Kazemier, G., additional
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- 2021
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7. Outcomes of Resectability Assessment of the Dutch Colorectal Cancer Group Liver Metastases Expert Panel
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Huiskens, J., Bolhuis, K., Engelbrecht, M.R.W., Jong, K.P. de, Kazemier, G., Liem, M.S.L., Verhoef, C., Wilt, J.H.W. de, Punt, C.J.A., Gulik, T.M. van, Amerongen, M.J. van, Dejong, C.H.C., Gerhards, M.F., Grunhagen, D., Heijmen, L., Hermans, J.J., Keijser, A., Klaase, J.M., Lienden, K.P. van, Molenaar, Q.I., Patijn, G.A., Rijken, A.M., Ruers, T.M., Swijnenbur, R.J., Tinteren, H. van, Dutch Colorectal Canc Grp, Surgery, Graduate School, AGEM - Endocrinology, metabolism and nutrition, AGEM - Re-generation and cancer of the digestive system, CCA - Cancer Treatment and Quality of Life, Radiology and Nuclear Medicine, Oncology, AGEM - Digestive immunity, MUMC+: MA Heelkunde (9), RS: NUTRIM - R2 - Liver and digestive health, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Guided Treatment in Optimal Selected Cancer Patients (GUTS), Groningen Institute for Organ Transplantation (GIOT), and Value, Affordability and Sustainability (VALUE)
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medicine.medical_specialty ,Colorectal cancer ,SURGERY ,Clinical Decision-Making ,MEDICAL ONCOLOGISTS ,HEPATIC RESECTION ,Systemic therapy ,law.invention ,Majority consensus ,Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14] ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,0302 clinical medicine ,Randomized controlled trial ,TUMOR ,SDG 3 - Good Health and Well-being ,Interquartile range ,law ,medicine ,Hepatectomy ,Humans ,In patient ,Prospective Studies ,Neoplasm Metastasis ,Prospective cohort study ,Neoplasm Staging ,business.industry ,General surgery ,Liver Neoplasms ,CHEMOTHERAPY ,Prognosis ,medicine.disease ,Radiography ,Clinical trial ,Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] ,030220 oncology & carcinogenesis ,SURVIVAL ,Feasibility Studies ,030211 gastroenterology & hepatology ,INTRAOPERATIVE ULTRASOUND ,Colorectal Neoplasms ,business ,Follow-Up Studies ,Rare cancers Radboud Institute for Health Sciences [Radboudumc 9] ,MRI ,CT - Abstract
BACKGROUND: Decision making on optimal treatment strategy in patients with initially unresectable colorectal cancer liver metastases (CRLM) remains complex because uniform criteria for (un) resectability are lacking. This study reports on the feasibility and short-term outcomes of The Dutch Colorectal Cancer Group Liver Expert Panel.STUDY DESIGN: The Expert Panel consists of 13 hepatobiliary surgeons and 4 radiologists. Resectability assessment is performed independently by 3 randomly assigned surgeons, and CRLM are scored as resectable, potentially resectable, or permanently unresectable. In absence of consensus, 2 additional surgeons are invited for a majority consensus. Patients with potentially resectable or unresectable CRLM at baseline are evaluated every 2 months of systemic therapy. Once CRLM are considered resectable, a treatment strategy is proposed.RESULTS: Overall, 398 panel evaluations in 183 patients were analyzed. The median time to panel conclusion was 7 days (interquartile range [IQR] 5-11 days). Intersurgeon disagreement was observed in 205 (52%) evaluations, with major disagreement (resectable vs permanently unresectable) in 42 (11%) evaluations. After systemic treatment, 106 patients were considered to have resectable CRLM, 84 of whom (79%) underwent a curative procedure. R0 resection (n = 41), R0 resection in combination with ablative treatment (n = 26), or ablative treatment only (n = 4) was achieved in 67 of 84 (80%) patients.CONCLUSIONS: This study analyzed prospective resectability evaluation of patients with CRLM by a panel of radiologists and liver surgeons. The high rate of disagreement among experienced liver surgeons reflects the complexity in defining treatment strategies for CRLM and supports the use of a panel rather than a single-surgeon decision. (C) 2019 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.
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- 2019
8. Implementation and outcomes of a national liver surgeons expert panel to determine secondary resectability in patients with initially unresectable colorectal liver metastases (CRLM)
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Swijnenburg, R., primary, Bolhuis, K., additional, Huiskens, J., additional, Van Lienden, K., additional, Engelbrecht, M., additional, Van Amerongen, M., additional, Heijmen, L., additional, Hermans, J., additional, Molenaar, Q., additional, Verhoef, C., additional, Grünhagen, D., additional, De Jong, K., additional, Klaase, J., additional, Dejong, C., additional, Kazemier, G., additional, Ruers, T., additional, De Wilt, H., additional, Rijken, A., additional, Gerhards, M., additional, Liem, M., additional, Patijn, G., additional, Punt, C., additional, and Van Gulik, T., additional
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- 2020
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9. A multicentre analysis on the impact of primary tumour location in patients undergoing surgery for colorectal liver metastasis.
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Galjart, B., primary, Olthof, P.B., additional, Boerner, T., additional, Buisman, F.E., additional, Nierop, P.M., additional, Huiskens, J., additional, van der Stok, E.P., additional, Allen, P.J., additional, Besselink, M.G., additional, Tanis, P.J., additional, Balanchandran, V.P., additional, Jarnagin, W.R., additional, Kingham, T.P., additional, Grünhagen, D.J., additional, D'Angelica, M.I., additional, van Gulik, T.M., additional, and Verhoef, C., additional
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- 2019
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10. Reporting of risk of bias at trial registration
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van Rosmalen, B.V., primary, Huiskens, J., additional, Bruns, E.R., additional, Besselink, M.G., additional, Punt, C.J., additional, Hooft, L., additional, and van Gulik, T.M., additional
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- 2019
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11. Portal vein embolization prior to resection of colorectal liver metastases does not impact oncological outcomes
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Huiskens, J., primary, Olthof, P.B., additional, van der Stok, E.P., additional, Bais, T., additional, van Amerongen, M., additional, van Lienden, K.P., additional, Krumreich, J., additional, Roumen, R., additional, Grünhagen, D.J., additional, Punt, C.J., additional, de Wilt, J.H., additional, Verhoef, C., additional, and van Gulik, T.M., additional
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- 2019
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12. Assessment of colorectal liver metastasis: results of the Dutch Colorectal Cancer Group (DCCG) liver metastases expert panel of the CAIRO5 study
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Huiskens, J., primary, Olthof, P.B., additional, Keijser, A., additional, van Lienden, K.P., additional, Engelbrecht, M.R., additional, Hermans, J.J., additional, Molenaar, I., additional, Verhoef, C., additional, de Jong, K., additional, Dejong, C., additional, Kazemier, G., additional, Ruers, T., additional, de Wilt, J., additional, Rijken, A., additional, Gerhards, M., additional, Liem, M., additional, Patijn, G., additional, van Oijen, M., additional, Punt, C., additional, and van Gulik, T., additional
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- 2019
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13. Machine Learning-based Auto-segmentation of Histological Residual Tumor in Resected Pancreatic Cancer after Neoadjuvant Therapy
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Janssen, B., Theijse, R., de Ruiter, R., Huiskens, J., Kazemier, G., Valkema, P., Farina, A., Verheij, J., Besselink, M., and de Boer, O.
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- 2021
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14. Who should not undergo alpps for colorectal liver metastases?
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Olthof, P., primary, Huiskens, J., additional, Schadde, E., additional, Lang, H., additional, Malago, M., additional, Petrowsky, H., additional, de Santibanes, E., additional, Oldhafer, K., additional, and van Gulik, T., additional
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- 2018
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15. Local approval procedures act as a brake on RCTs
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Stok, Eric, Huiskens, J, Hemmes, B, Grunhagen, DJ, Gulik, TM, Verhoef, Kees, Punt, CJA, and Surgery
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- 2016
16. Lokale toestemmingsprocedures zetten een rem op RCT's
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Stok, E. P., Huiskens, J., Hemmes, B., Dirk Grünhagen, Gulik, T. M., Cornelis Verhoef, Punt, C. J. A., Amsterdam Gastroenterology Endocrinology Metabolism, Surgery, Cancer Center Amsterdam, Oncology, and Erasmus MC other
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SDG 3 - Good Health and Well-being - Abstract
OBJECTIVE: Large multicentre randomised controlled trials (RCTs) in the Netherlands are increasingly being impeded by major differences between local approval procedures. However, no national agenda exists as yet to improve this situation. The existence of major local differences in processing time and documentation required has been reported previously but little is known about the costs incurred and whether or not specific certifications and research contracts are mandatory. The current study evaluated these aspects of local procedures for obtaining approval of two oncological multicentre RCTs. DESIGN: Retrospective, descriptive. METHOD: All local procedures for obtaining approval of two randomised clinical trials were evaluated: the CAIRO5 and CHARISMA trials initiated by the Dutch Colorectal Cancer Group (DCCG). We objectified time between approval by the Medical Ethics Review Committee (METC) and final approval by the Board of Directors (RvB), the type and number of documents needed, and costs charged. RESULTS: The median time interval between the approval by the Medical Ethics Review Committee and the approval by the Board of Directors was 90 days (range 4-312). The number of documents required per centre ranged from 6-20. The costs charged ranged from € 0-€ 1750, and amounted to € 8575 for all procedures combined. No costs were charged by the majority of the centres. CONCLUSION: The approval procedures for multicentre clinical trials in the Netherlands demonstrate major differences. Processing times, documentation required and costs are unpredictable; greater uniformity is highly desirable in this context.
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- 2016
17. Regional and inter-hospital differences in the utilisation of liver surgery for patients with synchronous colorectal liver metastases in the Netherlands
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't Lam-Boer, J., primary, van der Stok, E.P., additional, Huiskens, J., additional, Verhoeven, R.H.A., additional, Punt, C.J.A., additional, Elferink, M.A.G., additional, de Wilt, J.H., additional, Verhoef, C., additional, Dejong, C.H.C., additional, Grunhagen, D., additional, van Gulik, T.M., additional, de Jong, K.P., additional, Kazemier, G., additional, Molenaar, I.Q., additional, and Ruers, T.M., additional
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- 2017
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18. Hepatic vascular inflow occlusion is associated with reduced disease free survival following resection of colorectal liver metastases
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Olthof, P.B., primary, Huiskens, J., additional, Schulte, N.R., additional, Wicherts, D.A., additional, Besselink, M.G., additional, Busch, O.R.C., additional, Tanis, P.J., additional, and van Gulik, T.M., additional
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- 2017
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19. Is ALPPS an alternative for patients with otherwise unresectable colorectal liver metastases?
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Olthof, P.B., primary, Huiskens, J., additional, Wicherts, D.A., additional, Huespe, P., additional, Ardiles, V., additional, Robles-Campos, R., additional, Adam, R., additional, Clavien, P.-A., additional, Punt, C.J.A., additional, van Gulik, T.M., additional, and de Santibañes, E., additional
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- 2016
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20. The impact of a mobile application on awareness for multi-center clinical colorectal cancer trials: First results of the Dutch Colorectal Cancer Group (DCCG) Trialapp
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Huiskens, J., primary, Bakker, J.-M., additional, Coelen, R.J.S., additional, Olthof, P.B., additional, Schijven, M.P., additional, van Oijen, M.G.H., additional, van Gulik, T.M., additional, and Punt, C.J.A., additional
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- 2016
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21. Referral of patients with suspicion of perihilar cholangiocarcinoma to a tertiary center: A retrospective audit following introduction of a national management guideline
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Coelen, R., primary, Huiskens, J., additional, Rauws, E., additional, and van Gulik, T., additional
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- 2016
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22. Inter-observer variability of resectability assessment of colorectal liver metastasis: Preliminary Results of the Dutch Colorectal Cancer Group (DCCG) liver metastases expert panel
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Huiskens, J., primary, Punt, C.J.A., additional, and van Gulik, T.M., additional
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- 2016
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23. Logistic and ethical aspects of the Dutch nationwide colorectal liver metastases expert panel
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Huiskens, J., primary, Graafland, M., additional, Keijser, A., additional, Besselink, M.G.H., additional, van Gulik, T.M., additional, and Punt, C.J.A., additional
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- 2016
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24. VIABLE TUMOUR TISSUE ADHERENT TO NEEDLE APPLICATORS AFTER LOCAL ABLATION: A RISK FACTOR FOR LOCAL RECURRENCE
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Snoeren, N., Huiskens, J., Jansen, M., Rijken, A., Hillegersberg, R. van, Slooter, G., Klaase, J., Tol, P. van den, Erkel, A. van, and Kate, F. ten
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- 2010
25. Prognostic and predictive value of total tumor volume in patients with colorectal liver metastases.
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Zeeuw, M., Wesdorp, N., Ali, M., Voigt, K., Starmans, M., Roor, J., Waesberghe, J.-H. van, van den Bergh, J., Nota, I., Moos, S., Stoker, J., Grunhagen, D., Swijnenburg, R.-J., Punt, C., Huiskens, J., Verhoef, K., and Kazemier, G.
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- 2024
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26. Advancing total tumor volume estimation in colorectal liver metastases: development and evaluation of a self-learning auto-segmentation model.
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Zeeuw, M., Bereska, J., Wagenaar, L., van der Meulen, D., Wesdorp, N., Janssen, B., Besselink, M., Marquering, H., Waesberghe, J.-H. van, van den Bergh, J., Nota, I., Moos, S., Jenssen, H., Huiskens, J., Swijnenburg, R.-J., Punt, C., Stoker, J., Fretland, A., Kazemier, G., and Verpalen, I.
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- 2024
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27. Identifying genetic mutation status in patients with colorectal liver metastases using radiomics based machine learning models.
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Wesdorp, N.J., Zeeuw, J.M., van der Meulen, D., Erve, I. van 't, Bodalal, Z., Roor, J., van Waesberghe, J.H.T., Moos, S., van den Bergh, J., Nota, I., van Dieren, S., Stoker, J., Meijer, G.A., Swijnenburg, R.-J., Punt, C.J., Huiskens, J., Beets-Tan, R., Fijneman, R.J.A., Marquering, H.A., and Kazemier, G.
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- 2024
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28. Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation.
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Michiel Zeeuw J, Wesdorp NJ, Ali M, Bakker AJJ, Voigt KR, Starmans MPA, Roor J, Kemna R, van Waesberghe JHTM, van den Bergh JE, Nota IMGC, Moos SI, van Dieren S, van Amerongen MJ, Bond MJG, Chapelle T, van Dam RM, Engelbrecht MRW, Gerhards MF, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Kok NFM, Leclercq WKG, Liem MSL, van Lienden KP, Quintus Molenaar I, Patijn GA, Rijken AM, Ruers TM, de Wilt JHW, Verpalen IM, Stoker J, Grunhagen DJ, Swijnenburg RJ, Punt CJA, Huiskens J, Verhoef C, and Kazemier G
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- Humans, Male, Female, Middle Aged, Prognosis, Aged, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Adult, Liver Neoplasms secondary, Liver Neoplasms drug therapy, Liver Neoplasms diagnostic imaging, Colorectal Neoplasms pathology, Colorectal Neoplasms mortality, Tumor Burden, Neoplasm Recurrence, Local pathology
- Abstract
Background: This study aimed to assess the prognostic value of total tumor volume (TTV) for early recurrence (within 6 months) and overall survival (OS) in patients with colorectal liver metastases (CRLM), treated with induction systemic therapy followed by complete local treatment., Methods: Patients with initially unresectable CRLM from the multicenter randomized phase 3 CAIRO5 trial (NCT02162563) who received induction systemic therapy followed by local treatment were included. Baseline TTV and change in TTV as response to systemic therapy were calculated using the CT scan before and the first after systemic treatment, and were assessed for their added prognostic value. The findings were validated in an external cohort of patients treated at a tertiary center., Results: In total, 215 CAIRO5 patients were included. Baseline TTV and absolute change in TTV were significantly associated with early recurrence (P = 0.005 and P = 0.040, respectively) and OS in multivariable analyses (P = 0.024 and P = 0.006, respectively), whereas RECIST1.1 was not prognostic for early recurrence (P = 0.88) and OS (P = 0.35). In the validation cohort (n = 85), baseline TTV and absolute change in TTV remained prognostic for early recurrence (P = 0.041 and P = 0.021, respectively) and OS in multivariable analyses (P < 0.0001 and P = 0.012, respectively), and showed added prognostic value over conventional clinicopathological variables (increase C-statistic, 0.06; 95 % CI, 0.02 to 0.14; P = 0.008)., Conclusion: Total tumor volume is strongly prognostic for early recurrence and OS in patients who underwent complete local treatment of initially unresectable CRLM, both in the CAIRO5 trial and the validation cohort. In contrast, RECIST1.1 did not show prognostic value for neither early recurrence nor OS., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors of this manuscript declare relationships with the following companies: C.J.A.P. has an advisory role for Nordic Pharma; SAS Analytics paid for traveling expenses G. Kazemier. This funding is not related to the current research. The remaining authors declare no potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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29. Charting a new course in healthcare: early-stage AI algorithm registration to enhance trust and transparency.
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van Genderen ME, van de Sande D, Hooft L, Reis AA, Cornet AD, Oosterhoff JHF, van der Ster BJP, Huiskens J, Townsend R, van Bommel J, Gommers D, and van den Hoven J
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- 2024
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30. Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.
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Wesdorp NJ, Zeeuw JM, Postma SCJ, Roor J, van Waesberghe JHTM, van den Bergh JE, Nota IM, Moos S, Kemna R, Vadakkumpadan F, Ambrozic C, van Dieren S, van Amerongen MJ, Chapelle T, Engelbrecht MRW, Gerhards MF, Grunhagen D, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Liem MSL, van Lienden KP, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Marquering HA, Stoker J, Swijnenburg RJ, Punt CJA, Huiskens J, and Kazemier G
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- Humans, Prospective Studies, Tumor Burden, Clinical Trials as Topic, Colorectal Neoplasms diagnostic imaging, Deep Learning, Liver Neoplasms diagnostic imaging
- Abstract
Background: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM)., Methods: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated., Results: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation., Conclusions: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients., Relevance Statement: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency., Key Points: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations., (© 2023. The Author(s).)
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- 2023
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31. Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models.
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Wesdorp N, Zeeuw M, van der Meulen D, van 't Erve I, Bodalal Z, Roor J, van Waesberghe JH, Moos S, van den Bergh J, Nota I, van Dieren S, Stoker J, Meijer G, Swijnenburg RJ, Punt C, Huiskens J, Beets-Tan R, Fijneman R, Marquering H, Kazemier G, and On Behalf Of The Dutch Colorectal Cancer Group Liver Expert Panel
- Abstract
For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62-0.92), 0.77 (95%CI 0.64-0.90), 0.72 (95%CI 0.57-0.87), and 0.86 (95%CI 0.76-0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47-0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.
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- 2023
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32. Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study.
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van de Sande D, van Genderen ME, Verhoef C, Huiskens J, Gommers D, van Unen E, Schasfoort RA, Schepers J, van Bommel J, and Grünhagen DJ
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- Hospitalization, Humans, Machine Learning, ROC Curve, Retrospective Studies, Artificial Intelligence, Patient Discharge
- Abstract
Background: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept., Methods: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value., Results: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81-0.85]), sensitivity of 77.9% (0.67-0.87), specificity of 79.2% (0.72-0.85), positive predictive value of 61.6% (0.54-0.69), and negative predictive value of 89.3% (0.85-0.93)., Conclusion: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2022
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33. Interobserver Variability in CT-based Morphologic Tumor Response Assessment of Colorectal Liver Metastases.
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Wesdorp NJ, Kemna R, Bolhuis K, van Waesberghe JHTM, Nota IMGC, Struik F, Oulad Abdennabi I, Phoa SSKS, van Dieren S, van Amerongen MJ, Chapelle T, Dejong CHC, Engelbrecht MRW, Gerhards MF, Grünhagen D, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Liem MSL, van Lienden KP, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Swijnenburg RJ, Punt CJA, Huiskens J, Stoker J, and Kazemier G
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- Female, Humans, Male, Middle Aged, Observer Variation, Prospective Studies, Tomography, X-Ray Computed methods, Colorectal Neoplasms diagnostic imaging, Colorectal Neoplasms genetics, Liver Neoplasms diagnostic imaging, Liver Neoplasms drug therapy, Liver Neoplasms genetics
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Purpose To evaluate interobserver variability in the morphologic tumor response assessment of colorectal liver metastases (CRLM) managed with systemic therapy and to assess the relation of morphologic response with gene mutation status, targeted therapy, and Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 measurements. Materials and Methods Participants with initially unresectable CRLM receiving different systemic therapy regimens from the randomized, controlled CAIRO5 trial (NCT02162563) were included in this prospective imaging study. Three radiologists independently assessed morphologic tumor response on baseline and first follow-up CT scans according to previously published criteria. Two additional radiologists evaluated disagreement cases. Interobserver agreement was calculated by using Fleiss κ. On the basis of the majority of individual radiologic assessments, the final morphologic tumor response was determined. Finally, the relation of morphologic tumor response and clinical prognostic parameters was assessed. Results In total, 153 participants (median age, 63 years [IQR, 56-71]; 101 men) with 306 CT scans comprising 2192 CRLM were included. Morphologic assessment performed by the three radiologists yielded 86 (56%) agreement cases and 67 (44%) disagreement cases (including four major disagreement cases). Overall interobserver agreement between the panel radiologists on morphology groups and morphologic response categories was moderate (κ = 0.53, 95% CI: 0.48, 0.58 and κ = 0.54, 95% CI: 0.47, 0.60). Optimal morphologic response was particularly observed in patients treated with bevacizumab ( P = .001) and in patients with RAS/BRAF mutation ( P = .04). No evidence of a relationship between RECIST 1.1 and morphologic response was found ( P = .61). Conclusion Morphologic tumor response assessment following systemic therapy in participants with CRLM demonstrated considerable interobserver variability. Keywords: Tumor Response, Observer Performance, CT, Liver, Metastases, Oncology, Abdomen/Gastrointestinal Clinical trial registration no. NCT02162563 Supplemental material is available for this article. © RSNA, 2022.
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- 2022
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34. Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review.
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Janssen BV, Verhoef S, Wesdorp NJ, Huiskens J, de Boer OJ, Marquering H, Stoker J, Kazemier G, and Besselink MG
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- Biomarkers, Tumor, Humans, Prognosis, Treatment Outcome, Pancreatic Neoplasms, Adenocarcinoma diagnostic imaging, Adenocarcinoma therapy, Machine Learning, Models, Theoretical, Pancreatic Neoplasms diagnostic imaging, Pancreatic Neoplasms therapy
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Objective: To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC., Summary of Background Data: Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection., Methods: A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score., Results: After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36., Conclusions: The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)
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- 2022
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35. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.
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van de Sande D, Van Genderen ME, Smit JM, Huiskens J, Visser JJ, Veen RER, van Unen E, Ba OH, Gommers D, and Bommel JV
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- Humans, Artificial Intelligence, Biomedical Research
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Objective: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research., Methods: We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine., Conclusion: This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside., Competing Interests: Competing interests: DG received speaker's fees and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–2012), Maquet, and Novalung (medical advisory board 2015–2018). JH currently works as industry expert healthcare at SAS Institute. EvU currently works as principal analytics consultant at SAS Institute. No financial relationships exist that could be construed as a potential conflict of interest. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2022
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36. KRAS A146 Mutations Are Associated With Distinct Clinical Behavior in Patients With Colorectal Liver Metastases.
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van 't Erve I, Wesdorp NJ, Medina JE, Ferreira L, Leal A, Huiskens J, Bolhuis K, van Waesberghe JTM, Swijnenburg RJ, van den Broek D, Velculescu VE, Kazemier G, Punt CJA, Meijer GA, and Fijneman RJA
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- Aged, Analysis of Variance, Colorectal Neoplasms genetics, Female, Humans, Liver Neoplasms genetics, Male, Middle Aged, Mutation genetics, Neoplasm Metastasis physiopathology, Prognosis, Colorectal Neoplasms complications, Liver Neoplasms etiology, Neoplasm Metastasis genetics, Proto-Oncogene Proteins p21(ras) genetics
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Somatic KRAS mutations occur in approximately half of the patients with metastatic colorectal cancer (mCRC). Biologic tumor characteristics differ on the basis of the KRAS mutation variant. KRAS mutations are known to influence patient prognosis and are used as predictive biomarker for treatment decisions. This study examined clinical features of patients with mCRC with a somatic mutation in KRAS G12, G13, Q61, K117, or A146., Methods: A total of 419 patients with colorectal cancer with initially unresectable liver-limited metastases, who participated in a multicenter prospective trial, were evaluated for tumor tissue KRAS mutation status. For the subgroup of patients who carried a KRAS mutation and were treated with bevacizumab and doublet or triplet chemotherapy (N = 156), pretreatment circulating tumor DNA levels were analyzed, and total tumor volume (TTV) was quantified on the pretreatment computed tomography images., Results: Most patients carried a KRAS G12 mutation (N = 112), followed by mutations in G13 (N = 15), A146 (N = 12), Q61 (N = 9), and K117 (N = 5). High plasma circulating tumor DNA levels were observed for patients carrying a KRAS A146 mutation versus those with a KRAS G12 mutation, with median mutant allele frequencies of 48% versus 19%, respectively. Radiologic TTV revealed this difference to be associated with a higher tumor load in patients harboring a KRAS A146 mutation (median TTV 672 cm
3 [A146] v 74 cm3 [G12], P = .036). Moreover, KRAS A146 mutation carriers showed inferior overall survival compared with patients with mutations in KRAS G12 (median 10.7 v 26.4 months; hazard ratio = 2.5; P = .003)., Conclusion: Patients with mCRC with a KRAS A146 mutation represent a distinct molecular subgroup of patients with higher tumor burden and worse clinical outcomes, who might benefit from more intensive treatments. These results highlight the importance of testing colorectal cancer for all KRAS mutations in routine clinical care., Competing Interests: Remond J. A. Fijneman Research Funding: Merck BV (Inst), Personal Genome Diagnostics (Inst), Delfi Diagnostics (Inst), Cergentis (Inst) Patents, Royalties, Other Intellectual Property: Several Patents pending (Inst) No other potential conflicts of interest were reported. Remond J. A. Fijneman Research Funding: Merck BV (Inst), Personal Genome Diagnostics (Inst), Delfi Diagnostics (Inst), Cergentis (Inst) Patents, Royalties, Other Intellectual Property: Several Patents pending (Inst) No other potential conflicts of interest were reported., (© 2021 by American Society of Clinical Oncology.)- Published
- 2021
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37. The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment.
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Wesdorp NJ, Bolhuis K, Roor J, van Waesberghe JTM, van Dieren S, van Amerongen MJ, Chapelle T, Dejong CHC, Engelbrecht MRW, Gerhards MF, Grunhagen D, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Liem MSL, van Lienden KP, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Swijnenburg RJ, Punt CJA, Huiskens J, and Kazemier G
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Objectives: Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM)., Background: RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV., Methods: Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment., Results: A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease ( P = 0.015), while RECIST1.1 was not prognostic ( P = 0.821)., Conclusions: TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM., Competing Interests: C.J.A.P. has an advisory role for Nordic Pharma. This funding is not related to the current research. The remaining authors declare no potential conflicts of interest. The CAIRO5 study is supported by unrestricted scientific grants from Roche and Amgen. The funders had no role in the design, conduct, and submission of the study, or in the decision to submit the manuscript for publication., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)
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- 2021
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38. Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment.
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Janssen BV, Theijse R, van Roessel S, de Ruiter R, Berkel A, Huiskens J, Busch OR, Wilmink JW, Kazemier G, Valkema P, Farina A, Verheij J, de Boer OJ, and Besselink MG
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Background: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach., Methods: From 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy., Results: The highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82., Conclusions: This study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.
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- 2021
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39. Predicting need for hospital-specific interventional care after surgery using electronic health record data.
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van de Sande D, van Genderen ME, Verhoef C, van Bommel J, Gommers D, van Unen E, Huiskens J, and Grünhagen DJ
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- Administration, Intravenous, Aged, Anti-Bacterial Agents administration & dosage, Anti-Bacterial Agents therapeutic use, Female, Humans, Length of Stay statistics & numerical data, Male, Middle Aged, Neoplasms surgery, Patient Discharge statistics & numerical data, Postoperative Period, Reoperation statistics & numerical data, Retrospective Studies, Risk Factors, Surgical Oncology statistics & numerical data, Tertiary Care Centers, Time Factors, Electronic Health Records, Health Services Needs and Demand statistics & numerical data, Postoperative Care statistics & numerical data
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Background: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day., Methods: This study included all adult patients discharged from surgical care in the surgical oncology department from June 2017 to February 2020. The primary outcome was to predict whether a patient still needs hospital-specific interventional care beyond the second postoperative day. Hospital-specific care was defined as unplanned reoperations, radiological interventions, and intravenous antibiotics administration. Different analytical methods were compared with respect to the area under the receiver-operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value., Results: Each model was trained on 1,174 episodes. In total, 847 (50.5%) patients required an intervention during postoperative admission. A random forest model performed best with an area under the receiver-operating characteristics curve of 0.88 (95% confidence interval 0.83-0.93), sensitivity of 79.1% (95% confidence interval 0.67-0.92), specificity of 80.0% (0.73-0.87), positive predictive value of 57.6% (0.45-0.70) and negative predictive value of 91.7% (0.87-0.97)., Conclusion: This proof-of-concept study found that a random forest model could successfully predict whether a patient could be safely discharged to a nursing home and does not need hospital care anymore. Such a model could aid hospitals in addressing capacity challenges and improve patient flow, allowing for timely surgical care., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2021
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40. Generating insights in uncharted territories: real-time learning from data in critically ill patients-an implementer report.
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van de Sande D, Van Genderen ME, Huiskens J, Veen RER, Meijerink Y, Gommers D, and van Bommel J
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- Data Mining, Hospitalization, Humans, Intensive Care Units, COVID-19, Critical Illness
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Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future., Competing Interests: Competing interests: DG has received speakers fees and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–12), Maquet and Novalung (medical advisory board 2015–18). JH currently works as industry expert healthcare at SAS Institute. No financial relationships exists that could be construed as a potential conflict of interest. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2021
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41. Postoperative circulating tumour DNA is associated with pathologic response and recurrence-free survival after resection of colorectal cancer liver metastases.
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Bolhuis K, van 't Erve I, Mijnals C, Delis-Van Diemen PM, Huiskens J, Komurcu A, Lopez-Yurda M, van den Broek D, Swijnenburg RJ, Meijer GA, Punt CJA, and Fijneman RJA
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- Aged, Female, Humans, Liver Neoplasms secondary, Male, Middle Aged, Neoplasm Recurrence, Local epidemiology, Postoperative Complications epidemiology, Postoperative Period, Survival Analysis, Biomarkers, Tumor blood, Cell-Free Nucleic Acids blood, Colorectal Neoplasms pathology, Liver Neoplasms surgery, Neoplasm Recurrence, Local blood, Postoperative Complications blood
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Background: Recurrence rates after resection of colorectal cancer liver metastases (CRLM) are high and correlate with worse survival. Postoperative circulating tumour DNA (ctDNA) is a promising prognostic biomarker. Focusing on patients with resected CRLM, this study aimed to evaluate the association between the detection of postoperative ctDNA, pathologic response and recurrence-free survival (RFS)., Methods: Twenty-three patients were selected from an ongoing phase-3 trial who underwent resection of RAS-mutant CRLM after induction systemic treatment. CtDNA analysis was performed by droplet digital PCR using blood samples collected at baseline, before and after resection. Pathologic response of CRLM was determined via the Tumour Regression Grading system., Findings: With a median follow-up of 19.6 months, the median RFS for patients with detectable (N = 6, [26%]) and undetectable (N = 17, [74%]) postoperative ctDNA was 4.8 versus 12.1 months, respectively. Among 21 patients with available tumour tissue, pathologic response in patients with detectable compared to undetectable postoperative ctDNA was found in one of six (17%) and 15 of 15 (100%) patients, respectively (p < 0.001). In univariable Cox regression analyses both postoperative detectable ctDNA (HR = 3.3, 95%CI = 1.1-9.6, p = 0.03) and pathologic non-response (HR = 4.6, 95%CI = 1.4-15, p = 0.01) were associated with poorer RFS and were strongly correlated (r = 0.88, p < 0.001). After adjusting for clinical characteristics in pairwise multivariable analyses, postoperative ctDNA status remained associated with RFS., Interpretation: The detection of postoperative ctDNA after secondary resection of CRLM is a promising prognostic factor for RFS and appeared to be highly correlated with pathologic response., Funding: None., Competing Interests: Declaration of Competing Interest C.J.A.P. has an advisory role for Nordic Pharma. This funding is not related to the current research. G.A.M. reports non-financial support from Exact Sciences, non-financial support from Sysmex, non-financial support from Sentinel CH. SpA, non-financial support from Personal Genome Diagnostics (PGDX), other from Hartwig Medical Foundation, grants from CZ (OWM Centrale Zorgverzekeraars groep Zorgverzekeraar u.a), other from Royal Philips, other from GlaxoSmithKline, other from Keosys SARL, other from Open Clinica LLC, other from Roche Diagnostics Nederland BV, other from The Hyve BV, other from Open Text, other from SURFSara BV, other from Vancis BV, other from CSC Computer Sciences BV, outside the submitted work; In addition, G.A.M. has several patents pending. R.J.A.F. reports grants and non-financial support from Personal Genome Diagnostics, grants from MERCK BV, non-financial support from Pacific Biosciences, non-financial support from Cergentis BV, outside the submitted work; In addition, R.J.A.F. has several patents pending. The remaining authors declare no potential conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)
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- 2021
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42. Short-Term Outcomes of Secondary Liver Surgery for Initially Unresectable Colorectal Liver Metastases Following Modern Induction Systemic Therapy in the Dutch CAIRO5 Trial.
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Bolhuis K, Grosheide L, Wesdorp NJ, Komurcu A, Chapelle T, Dejong CHC, Gerhards MF, Grünhagen DJ, van Gulik TM, Huiskens J, De Jong KP, Kazemier G, Klaase JM, Liem MSL, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Punt CJA, and Swijnenburg RJ
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Objective: To present short-term outcomes of liver surgery in patients with initially unresectable colorectal liver metastases (CRLM) downsized by chemotherapy plus targeted agents., Background: The increase of complex hepatic resections of CRLM, technical innovations pushing boundaries of respectability, and use of intensified induction systemic regimens warrant for safety data in a homogeneous multicenter prospective cohort., Methods: Patients with initially unresectable CRLM, who underwent complete resection after induction systemic regimens with doublet or triplet chemotherapy, both plus targeted therapy, were selected from the ongoing phase III CAIRO5 study (NCT02162563). Short-term outcomes and risk factors for severe postoperative morbidity (Clavien Dindo grade ≥ 3) were analyzed using logistic regression analysis., Results: A total of 173 patients underwent resection of CRLM after induction systemic therapy. The median number of metastases was 9 and 161 (93%) patients had bilobar disease. Thirty-six (20.8%) 2-stage resections and 88 (51%) major resections (>3 liver segments) were performed. Severe postoperative morbidity and 90-day mortality was 15.6% and 2.9%, respectively. After multivariable analysis, blood transfusion (odds ratio [OR] 2.9 [95% confidence interval (CI) 1.1-6.4], P = 0.03), major resection (OR 2.9 [95% CI 1.1-7.5], P = 0.03), and triplet chemotherapy (OR 2.6 [95% CI 1.1-7.5], P = 0.03) were independently correlated with severe postoperative complications. No association was found between number of cycles of systemic therapy and severe complications ( r = -0.038 , P = 0.31)., Conclusion: In patients with initially unresectable CRLM undergoing modern induction systemic therapy and extensive liver surgery, severe postoperative morbidity and 90-day mortality were 15.6% and 2.7%, respectively. Triplet chemotherapy, blood transfusion, and major resections were associated with severe postoperative morbidity., Competing Interests: Disclosure: C.J.A.P. has an advisory role for Nordic Pharma. The other authors declare that they have nothing to disclose. The CAIRO5 study (NCT02162563) was supported by unrestricted scientific grants from Roche and Amgen. The funders had no role in the design, conduct and submission of the study, nor in the decision to submit the manuscript for publication., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)
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- 2021
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43. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.
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van de Sande D, van Genderen ME, Huiskens J, Gommers D, and van Bommel J
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- Humans, Observational Studies as Topic, Retrospective Studies, Artificial Intelligence, Intensive Care Units
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Purpose: Due to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adequate and efficient clinical decision-making. The advancement of artificial intelligence (AI) technologies has resulted in the development of prediction models, which might aid clinical decision-making. This systematic review seeks to give a contemporary overview of the current maturity of AI in the ICU, the research methods behind these studies, and the risk of bias in these studies., Methods: A systematic search was conducted in Embase, Medline, Web of Science Core Collection and Cochrane Central Register of Controlled Trials databases to identify eligible studies. Studies using AI to analyze ICU data were considered eligible. Specifically, the study design, study aim, dataset size, level of validation, level of readiness, and the outcomes of clinical trials were extracted. Risk of bias in individual studies was evaluated by the Prediction model Risk Of Bias ASsessment Tool (PROBAST)., Results: Out of 6455 studies identified through literature search, 494 were included. The most common study design was retrospective [476 studies (96.4% of all studies)] followed by prospective observational [8 (1.6%)] and clinical [10 (2%)] trials. 378 (80.9%) retrospective studies were classified as high risk of bias. No studies were identified that reported on the outcome evaluation of an AI model integrated in routine clinical practice., Conclusion: The vast majority of developed ICU-AI models remain within the testing and prototyping environment; only a handful were actually evaluated in clinical practice. A uniform and structured approach can support the development, safe delivery, and implementation of AI to determine clinical benefit in the ICU.
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- 2021
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44. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.
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Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, and Kazemier G
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- Humans, Artificial Intelligence, Gastrointestinal Neoplasms diagnostic imaging, Gastrointestinal Neoplasms therapy
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Purpose: Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors., Methods: A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score., Results: The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice., Conclusion: Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.
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- 2021
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45. Predicting thromboembolic complications in COVID-19 ICU patients using machine learning.
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van de Sande D, van Genderen ME, Rosman B, Diether M, Endeman H, van den Akker JPC, Ludwig M, Huiskens J, Gommers D, and van Bommel J
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Background: The coronavirus disease 2019 (COVID-19) pandemic is a challenge for intensive care units (ICU) in part due to the failure to identify risks for patients early and the inability to render an accurate prognosis. Previous reports suggest a strong association between hypercoagulability and poor outcome. Factors related to hemostasis may, therefore, serve as tools to improve the management of COVID-19 patients., Aim: The purpose of this report is to develop a model to determine whether it is possible to early identify COVID-19 patients at risk for thromboembolic complications (TCs)., Methods: We analyzed electronic health record data of 108 consecutive COVID-19 patients admitted to the adult ICU of the Erasmus University Medical Center between February 27 and May 20, 2020. By training a decision tree classifier on 66% of the available data, a model for the prediction of TCs was developed., Results: The median (interquartile range) age was 62 (53-70) years and 73% were male. Forty-three patients (40%) developed a TC during their ICU stay. Mortality was higher for patients in the TCs group compared to the control group (26% vs. 8%, P =0.03). Lactate dehydrogenase, standardized bicarbonate, albumin, and leukocytes were identified by the Decision Tree classifier as the most powerful predictors for TCs 2 days before the onset of the TC, with a sensitivity of 73% and a positive likelihood ratio of 2.7 on the test dataset., Conclusions: Clinically relevant TCs frequently occur in critically ill COVID-19 patients. These can successfully be predicted using a decision tree model. Although this model could be of special importance to aid clinical decision making, its generalizability and clinical impact should be determined in a larger population., Relevance for Patients: Recently, severe TCs were observed in COVID-19 patients with progressive respiratory failure warranting ICU treatment. Timely identification of patients at risk of developing TCs is critical inasmuch as it would enable clinicians to initiate potentially salvaging therapeutic anticoagulation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright: © Whioce Publishing Pte. Ltd.)
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- 2020
46. From registration to publication: A study on Dutch academic randomized controlled trials.
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Huiskens J, Kool BRJ, Bakker JM, Bruns ERJ, de Jonge SW, Olthof PB, van Rosmalen BV, van Gulik TM, Hooft L, and Punt CJA
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- Academic Medical Centers, Databases, Factual, Humans, Kaplan-Meier Estimate, Netherlands, PubMed, Publication Bias, Publishing, Registries, Reproducibility of Results, Research Personnel, Research Report, Sample Size, Publications, Randomized Controlled Trials as Topic, Research Design
- Abstract
Introduction: Registration of clinical trials has been initiated in order to assess adherence of the reported results to the original trial protocol. This study aimed to investigate the publication rates, timely dissemination of results, and the prevalence of consistency in hypothesis, sample size, and primary endpoint of Dutch investigator-initiated randomized controlled clinical trials (RCTs)., Methods: All Dutch investigator-initiated RCTs with a completion date between December 31, 2010, and January 1, 2012, and registered in the Trial Register of The Netherlands database were included. PubMed was searched for the publication of these RCT results until September 2016, and the time to the publication date was calculated. Consistency in hypothesis, sample size, and primary endpoint compared with the registry data were assessed., Results: The search resulted in a total of 168 Dutch investigator-initiated RCTs. In September 2016, the results of 129 (77%) trials had been published, of which 50 (39%) within 2 years after completion of accrual. Consistency in hypothesis with the original protocol was observed in 108 (84%) RCTs; in 71 trials (55%), the planned sample size was reached; and 103 trials (80%) presented the original primary endpoint. Consistency in all three parameters was observed in 50 studies (39%)., Conclusion: This study shows that approximately one out of four Dutch investigator-initiated RCTs remains unpublished 5 years after initiation. The observed low overall consistency with the initial study outline is a matter of concern and warrants improvements in trial design and assessment of trial feasibility., (© 2019 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)
- Published
- 2020
- Full Text
- View/download PDF
47. Outcomes of Resectability Assessment of the Dutch Colorectal Cancer Group Liver Metastases Expert Panel.
- Author
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Huiskens J, Bolhuis K, Engelbrecht MR, De Jong KP, Kazemier G, Liem MS, Verhoef C, de Wilt JH, Punt CJ, and van Gulik TM
- Subjects
- Colorectal Neoplasms surgery, Feasibility Studies, Follow-Up Studies, Humans, Liver Neoplasms diagnosis, Liver Neoplasms secondary, Neoplasm Metastasis, Neoplasm Staging, Prognosis, Prospective Studies, Radiography, Clinical Decision-Making, Colorectal Neoplasms pathology, Hepatectomy methods, Liver Neoplasms surgery
- Abstract
Background: Decision making on optimal treatment strategy in patients with initially unresectable colorectal cancer liver metastases (CRLM) remains complex because uniform criteria for (un)resectability are lacking. This study reports on the feasibility and short-term outcomes of The Dutch Colorectal Cancer Group Liver Expert Panel., Study Design: The Expert Panel consists of 13 hepatobiliary surgeons and 4 radiologists. Resectability assessment is performed independently by 3 randomly assigned surgeons, and CRLM are scored as resectable, potentially resectable, or permanently unresectable. In absence of consensus, 2 additional surgeons are invited for a majority consensus. Patients with potentially resectable or unresectable CRLM at baseline are evaluated every 2 months of systemic therapy. Once CRLM are considered resectable, a treatment strategy is proposed., Results: Overall, 398 panel evaluations in 183 patients were analyzed. The median time to panel conclusion was 7 days (interquartile range [IQR] 5-11 days). Intersurgeon disagreement was observed in 205 (52%) evaluations, with major disagreement (resectable vs permanently unresectable) in 42 (11%) evaluations. After systemic treatment, 106 patients were considered to have resectable CRLM, 84 of whom (79%) underwent a curative procedure. R0 resection (n = 41), R0 resection in combination with ablative treatment (n = 26), or ablative treatment only (n = 4) was achieved in 67 of 84 (80%) patients., Conclusions: This study analyzed prospective resectability evaluation of patients with CRLM by a panel of radiologists and liver surgeons. The high rate of disagreement among experienced liver surgeons reflects the complexity in defining treatment strategies for CRLM and supports the use of a panel rather than a single-surgeon decision., (Copyright © 2019 American College of Surgeons. Published by Elsevier Inc. All rights reserved.)
- Published
- 2019
- Full Text
- View/download PDF
48. Avoiding postoperative mortality after ALPPS-development of a tumor-specific risk score for colorectal liver metastases.
- Author
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Huiskens J, Schadde E, Lang H, Malago M, Petrowsky H, de Santibañes E, Oldhafer K, van Gulik TM, and Olthof PB
- Subjects
- Aged, Argentina, Colorectal Neoplasms mortality, Europe, Female, Hepatectomy adverse effects, Humans, Ligation, Liver Neoplasms mortality, Liver Neoplasms secondary, Liver Regeneration, Male, Middle Aged, Portal Vein pathology, Postoperative Complications prevention & control, Registries, Retrospective Studies, Risk Assessment, Risk Factors, Time Factors, Treatment Outcome, Vascular Surgical Procedures adverse effects, Colorectal Neoplasms pathology, Hepatectomy mortality, Liver Neoplasms surgery, Portal Vein surgery, Postoperative Complications mortality, Vascular Surgical Procedures mortality
- Abstract
Background: ALPPS is a two-stage hepatectomy that induces more rapid liver growth compared to conventional strategies. This report aims to establish a risk-score to avoid adverse outcomes of ALPPS only for patients with colorectal liver metastases (CRLM) as primary indication for ALPPS., Methods: All patients with CRLM included in the ALPPS registry were included. Risk score analysis was performed for 90-day mortality after ALPPS, defined as death within 90 days after either stage. Two risk scores were generated i.e. one for application before stage-1, and one for application before stage-2. Logistic regression analysis was performed to establish the risk-score., Results: In total, 486 patients were included, of which 35 (7%) died 90 days after stage-1 or 2. In the stage-1 risk score, age ≥67 years (OR 3.7), FLR/BW ratio <0.40 (OR 2.9) and total center-volume (OR 2.4) were included. For the stage-2 score age ≥67 years (OR 3.7), FLR/BW ratio <0.40 (OR 2.8), bilirubin 5 days after stage-1 >50 μmol/L (OR 2.4), and stage-1 morbidity grade IIIA or higher (OR 6.3) were included., Conclusions: The CRLM risk-score to predict mortality after ALPPS demonstrates that older patients with small remnant livers in inexperienced centers, especially after experiencing morbidity after stage-1 have adverse outcomes. The risk score may be used to restrict ALPPS to low-risk patient populations., (Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)
- Published
- 2019
- Full Text
- View/download PDF
49. Keeping track of all ongoing colorectal cancer trials using a mobile application: Usability and satisfaction results of the Dutch Colorectal Cancer Group Trials application.
- Author
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Huiskens J, Gałek-Aldridge MS, Bakker JM, Olthof PB, van Gulik TM, Punt CJA, and van Oijen MGH
- Abstract
Background and Aim: Both the number and complexity of medical trials are increasing vastly. To facilitate easy access to concise trial information, a freely available mobile application including all ongoing clinical trials of the Dutch Colorectal Cancer Group (DCCG) was developed. The aim of this study was to investigate the use and user satisfaction over the first 2 years., Methods: The application was launched in January 2015 on iOS and Android platforms. Google Analytics was used to monitor anonymous user data up to February 2017. In addition, an online survey regarding the use and satisfaction among health-care professionals and research affiliates active in the field of colorectal cancer in the Netherlands was conducted., Results: A total of 6173 unique users were identified, of which 1822 (30%) were from the Netherlands, representing a total of 16,065 and 10,987 (68%) sessions, respectively. The median session duration per day was 01:47 min (IQR 0:51-03:03). The mobile application was mostly used on Monday, Tuesday, and Thursday, and the number of sessions was highest during the following time frames: 12-13 pm (9%), 17-18 pm (9%), and 13-14 pm (8%). Of 121 survey responses, most were medical doctors (47%), nurses (25%), or researchers (9%), working either in a teaching (40%), academic hospital (32%), or general hospital (19%). 83% of all respondents rated the application 4 or higher for satisfaction on a 5-point scale. Highest reported reasons of the use were urgent trial inquiry (57%) and usage during multi-disciplinary meetings (49%)., Conclusion: The DCCG Trials application is frequently used, and the majority of users is highly satisfied., Relevance for Patients: Clustering trial information into one platform, such as DCCG trials app, has shown to be useful for medical professionals treating patients with colorectal carcinoma in the Netherlands.
- Published
- 2018
50. Pseudoprogression on bevacizumab treatment: tumor-dynamics in the modern era of systemic treatment for metastatic colorectal cancer.
- Author
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Punt CJA, Huiskens J, van Gulik T, and Engelbrecht M
- Subjects
- Antineoplastic Combined Chemotherapy Protocols therapeutic use, Colorectal Neoplasms pathology, Disease Progression, Humans, Male, Middle Aged, Neoplasm Metastasis, Remission Induction methods, Treatment Outcome, Withholding Treatment, Bevacizumab therapeutic use, Colorectal Neoplasms drug therapy, Tumor Burden drug effects
- Published
- 2018
- Full Text
- View/download PDF
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