Contour+
Dahlstedt-Hassler, L., Siegbahn, A., Kojoj, R., Schedin, A., Lagerbäck, C., & Lind, P. (2026). Qualitative and quantitative evaluation of auto-segmented clinical target volumes and organs at risk in radiotherapy of rectal cancer. Journal of Radiotherapy in Practice, 25, e9. https://doi.org/10.1017/S1460396926100405
Kim, S., Lee, Y. K., Cheon, W., Seol, Y., Choi, B.-O., & Kang, Y. N. (2026). Quantitative assessment of CBCT image quality metrics and their translation to clinical ART workflow performance in breast cancer patients. Journal of Applied Clinical Medical Physics, 27(6), e70648. https://doi.org/10.1002/acm2.70648
Miovecz, A., Nagy, B., Gugyeras, D., Fajtai, D., Sipos, D., Vandulek, C., Kovacs, A., & Lakosi, F. (2026). Clinical evaluation of three deep learning contouring models for head and neck organs at risk and lymph node levels. Technical Innovations & Patient Support in Radiation Oncology, 40, 100437. https://doi.org/10.1016/j.tipsro.2026.100437
Chick, J., Casey, F., Cooper, S., Herbert, T., Alexander, S., Predescu, N., Lőrincz-Molnár, S.-B., Nill, S., Oelfke, U., Tree, A., & Dunlop, A. (2025). Towards rapid and efficient simulation-free radiotherapy: MR guided adaptive prostate radiotherapy on the MR-Linac using diagnostic MRI reference planning. Radiotherapy and Oncology, 211, 111053. https://doi.org/10.1016/j.radonc.2025.111053
Emin, S., Rossi, E., Hedman, M., Giovenco, M., Villegas, F., & Onjukka, E. (2025). Performance of multi-vendor auto-segmentation models for thoracic organs at risk trained on a single dataset. Physica Medica, 137, 105089. https://doi.org/10.1016/j.ejmp.2025.105089
Langmack, K. A., Alexander, G. G., Gardiner, J., McKenna, A., & Shawcroft, E. (2025). An audit of the impact of the introduction of a commercial artificial intelligence-driven auto-contouring tool into a radiotherapy department. British Journal of Radiology, 98(1167), 375–382. https://doi.org/10.1093/bjr/tqae255
Malone, C., Nicholson, J., Ryan, S., Thirion, P., Woods, R., McBride, P., McArdle, O., Duane, F., Hanna, G. G., McClean, B., & Brennan, S. (2025). Real world AI-driven segmentation: Efficiency gains and workflow challenges in radiotherapy. Radiotherapy and Oncology, 209, 110977. https://doi.org/10.1016/j.radonc.2025.110977
Mason, J., Doherty, J., Robinson, S., de la Bastide, M., Miskell, J., & McLauchlan, R. (2025). Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy. Physics and Imaging in Radiation Oncology, 33, 100716. https://doi.org/10.1016/j.phro.2025.100716
Miura, H., Ishihara, S., Kenjo, M., Nakao, M., Ozawa, S., & Kagemoto, M. (2025). Evaluation of the accuracy of automated segmentation based on Deep Learning for Prostate Cancer Patients. Medical Dosimetry, 50(1), 91–95. https://doi.org/10.1016/j.meddos.2024.09.002
Miura, H., Ishihara, S., Kenjo, M., Nakao, M., Ozawa, S., & Kagemoto, M. (2025b). Performance evaluation of MVision AI Contour+ in gastric MALT lymphoma segmentation. Reports of Practical Oncology and Radiotherapy, 30(1), 122–125. https://doi.org/10.5603/rpor.104144
Mövik, L., Bäck, A., Gunnarsson, K., Jamtheim Gustafsson, C., Hallqvist, A., & Pettersson, N. (2025). A semi-automated workflow for cohort-wise preparation of radiotherapy data for dose-response modeling, including autosegmentation of organs at risk. Journal of Applied Clinical Medical Physics, 26, e70152. https://doi.org/10.1002/acm2.70152
Pang, E. P., Tan, H. Q., Wang, F., Niemelä, J., Bolard, G., Ramadan, S., Kiljunen, T., Capala, M., Petit, S., Seppälä, J., Vuolukka, K., Kiitam, I., Zolotuhhin, D., Gershkevitsh, E., Lehtiö, K., Nikkinen, J., Keyriläinen, J., Mokka, M., & Chua, M. L. (2025). Multicentre evaluation of deep learning CT Autosegmentation of the head and neck region for radiotherapy. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01624-z
Tang, V., Wieslander, E., Haghanegi, M., Kjellén, E., & Alkner, S. (2025). Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk. Clinical and Translational Radiation Oncology, 53, 100986. https://doi.org/10.1016/j.ctro.2025.100986
Bordigoni, B., Trivellato, S., Pellegrini, R., Meregalli, S., Bonetto, E., Belmonte, M., Castellano, M., Panizza, D., Arcangeli, S., & De Ponti, E. (2024). Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of atlas-, machine learning-, and Deep Learning-based models. Physica Medica, 125, 104486. https://doi.org/10.1016/j.ejmp.2024.104486
Bungyi, G., Pócza, T., Zongor, Á., Takácsi-Nagy, Z., & Major, T. (2024). Mesterséges intelligencián alapuló szervkontúrozó szoftverek alkalmazása és eredményeinek összehasonlítása sugárterápiás besugárzástervezés során [Application of artificial intelligence-based organ contouring software and comparison of results in treatment planning for radiotherapy]. Orvosi Hetilap, 165(49), 1934–1944. https://doi.org/10.1556/650.2024.33180
Meixner, E., Glogauer, B., Klüter, S., Wagner, F., Neugebauer, D., Hoeltgen, L., Dinges, L. A., Harrabi, S., Liermann, J., Vinsensia, M., Weykamp, F., Hoegen-Saßmannshausen, P., Debus, J., & Hörner-Rieber, J. (2024). Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation. Clinical and Translational Radiation Oncology, 49, 100855. https://doi.org/10.1016/j.ctro.2024.100855
Melerowitz, L., Sreenivasa, S., Nachbar, M., Stsefanenka, A., Beck, M., Senger, C., Predescu, N., Ullah Akram, S., Budach, V., Zips, D., Heiland, M., Nahles, S., & Stromberger, C. (2024). Design and evaluation of a Deep Learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net. Clinical and Translational Radiation Oncology, 47, 100780. https://doi.org/10.1016/j.ctro.2024.100780
Meyer, C., Huger, S., Bruand, M., Leroy, T., Palisson, J., Rétif, P., Sarrade, T., Barateau, A., Renard, S., Jolnerovski, M., Demogeot, N., Marcel, J., Martz, N., Stefani, A., Sellami, S., Jacques, J., Agnoux, E., Gehin, W., Trampetti, I., … Faivre, J.-C. (2024). Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas. Radiation Oncology, 19(1). https://doi.org/10.1186/s13014-024-02554-y
Doolan, P. J., Charalambous, S., Roussakis, Y., Leczynski, A., Peratikou, M., Benjamin, M., Ferentinos, K., Strouthos, I., Zamboglou, C., & Karagiannis, E. (2023). A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1213068
Strolin, S., Santoro, M., Paolani, G., Ammendolia, I., Arcelli, A., Benini, A., Bisello, S., Cardano, R., Cavallini, L., Deraco, E., Donati, C. M., Galietta, E., Galuppi, A., Guido, A., Ferioli, M., Laghi, V., Medici, F., Ntreta, M., Razganiayeva, N., … Strigari, L. (2023). How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved deep-learning tool using multiple expert contours delineated on planning CT images. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1089807
Turcas, A., Leucuta, D., Balan, C., Clementel, E., Gheara, C., Kacso, A., Kelly, S. M., Tanasa, D., Cernea, D., & Achimas-Cadariu, P. (2023). Deep-Learning Magnetic Resonance Imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution. Physics and Imaging in Radiation Oncology, 27, 100454. https://doi.org/10.1016/j.phro.2023.100454
Warren, S., Richmond, N., Wowk, A., Wilkinson, M., & Wright, K. (2023). AI segmentation as a quality improvement tool in radiotherapy planning for breast cancer. IPEM-Translation, 6–8, 100020. https://doi.org/10.1016/j.ipemt.2023.100020
Olsson, C.E., Suresh, R., Niemelä, J., Akram, S. U., & Valdman, A. (2022). Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy. Physics and Imaging in Radiation Oncology, 22, 67–72. https://doi.org/10.1016/j.phro.2022.04.007
Kiljunen, T., Akram, S., Niemelä, J., Löyttyniemi, E., Seppälä, J., Heikkilä, J., Vuolukka, K., Kääriäinen, O.-S., Heikkilä, V.-P., Lehtiö, K., Nikkinen, J., Gershkevitsh, E., Borkvel, A., Adamson, M., Zolotuhhin, D., Kolk, K., Pang, E. P. P., Tuan, J. K. L., Master, Z., … Keyriläinen, J. (2020). A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics, 10(11), 959. https://doi.org/10.3390/diagnostics10110959
