Article Versions Notes

Geitenbeek RTJ, Baltus SC, Broekman M, Barendsen SN, Frieben MC, Asaggau I, Thibeau-Sutre E, Wolterink JM, Vermeulen MC, Tan CO, et al. Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection. Cancers. 2025; 17(6):1051. https://doi.org/10.3390/cancers17061051

Geitenbeek, Ritch T. J., Simon C. Baltus, Mark Broekman, Sander N. Barendsen, Maike C. Frieben, Ilias Asaggau, Elina Thibeau-Sutre, Jelmer M. Wolterink, Matthijs C. Vermeulen, Can O. Tan, and et al. 2025. "Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection" Cancers 17, no. 6: 1051. https://doi.org/10.3390/cancers17061051

Geitenbeek, R. T. J., Baltus, S. C., Broekman, M., Barendsen, S. N., Frieben, M. C., Asaggau, I., Thibeau-Sutre, E., Wolterink, J. M., Vermeulen, M. C., Tan, C. O., Broeders, I. A. M. J., & Consten, E. C. J., on behalf of the MIRECA Study Group. (2025). Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection. Cancers, 17(6), 1051. https://doi.org/10.3390/cancers17061051