ANALYSIS OF CT VOLUMETRY AND RECIST 1.1 CRITERIA IN THE EVALUATION OF METASTATIC LIVER DYNAMICS
DOI:
https://doi.org/10.32689/2663-0672-2025-2-5Keywords:
liver metastases, RECIST 1.1, CT volumetry, assessment of therapeutic response, computed tomography, tumour growthAbstract
Assessment of tumour response to treatment is an important aspect of modern oncology. The current RECIST 1.1 criteria, which are based on the measurement of linear tumour size, have limitations, especially when assessing complex tumours. CT volumetry, which allows to assess changes in tumour volume, is a promising method for more accurate tracking of the dynamics of metastatic liver lesions.Methods. The study included 10 patients with metastatic liver disease. Each patient underwent several examinations with an interval of 1-3 months, during which the maximum diameters were measured according to RECIST 1.1 criteria and the volume of metastatic foci was measured using CT volumetry. Statistical analysis was used to assess the correlation between the results of the two methods.Results. A high level of correlation was found between RECIST 1.1 scores and CT volumetry. In 90% of cases, the response categories for both methods coincided. However, CT volumetry showed greater sensitivity to changes in tumour volumes, allowing for more accurate tracking of even minor changes. In cases of asymmetric tumour growth or changes in their shape, volumetry was more sensitive than linear measurements.Conclusions. CT volumetry is a more sensitive method for assessing the dynamics of liver metastatic foci compared to the traditional RECIST 1.1 criteria. It has the ability to detect early response to treatment, which allows timely adjustment of treatment tactics. The combined use of both methods can provide a more accurate and complete assessment of the patient's condition.
References
Buckler A. J., Mulshine J. L., Gottlieb R., Zhao B., Mozley P. D., Schwartz L. The use of volumetric CT as an imaging biomarker in lung cancer. Acad Radiol. 2010. 17(1), 100–6. doi: 10.1016/j.acra.2009.07.030.
Debela D. T., Muzazu S. G., Heraro K. D., Ndalama M. T., Mesele B. W., Haile D. C., Kitui S. K., Manyazewal T. New approaches and procedures for cancer treatment: Current perspectives. SAGE Open Med. 2021. 9, 20503121211034366. doi: 10.1177/20503121211034366.
Fournier L., de Geus-Oei L. F., Regge D., Oprea-Lager D. E., D'Anastasi M., Bidaut L., Bäuerle T., Lopci E., Cappello G., Lecouvet F., Mayerhoefer M., Kunz W. G., Verhoeff JJC., Caruso D., Smits M., Hoffmann R. T., Gourtsoyianni S., Beets-Tan R., Neri E., deSouza N. M., Deroose C. M., Caramella C. Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group – ESOI Joint Paper. Front Oncol. 2022. 11, 800547. doi: 10.3389/fonc.2021.800547.
Goldmacher G. V., Conklin J. The use of tumour volumetrics to assess response to therapy in anticancer clinical trials. Br J Clin Pharmacol. 2012. 73(6), 846–854. doi: 10.1111/j.1365-2125.2012.04179.x.
Iannessi A., Beaumont H., Ojango C., Bertrand A. S., Liu Y. RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads. Insights Imaging. 2024. 15(1), 199. doi: 10.1186/s13244-024-01774-w.
Jester N., Singh M., Lorr S., Tommasini S. M., Wiznia D. H., Buono F. D. The development of an artificial intelligence auto- segmentation tool for 3D volumetric analysis of vestibular schwannomas. Sci Rep. 2025. 15(1), 5918. doi: 10.1038/s41598- 025-88589-x.
Machado L., Alberge L., Philippe H., Ferreres E., Khlaut J., Dupuis J., Le Floch K., Habip Gatenyo D., Roux P., Grégory J., Ronot M., Dancette C., Boeken T., Tordjman D., Manceron P., Hérent P. A promptable CT foundation model for solid tumor evaluation. NPJ Precis Oncol. 2025. 9(1), 121. doi: 10.1038/s41698-025-00903-y.
Stephe S., Kumar S. B., Thirumalraj A., Dzhyvak V. Transformer based attention guided network for segmentation and hybrid network for classification of liver tumor from CT scan images. East Ukr Med J. 2024. 12(3),692–710. doi: 10.21272/eumj.2024;12(3):692-710.
Wesdorp N. J., Zeeuw J. M., Postma SCJ., Roor J., van Waesberghe JHTM., van den Bergh J. E., Nota I. M., Moos S., Kemna R., Vadakkumpadan F., Ambrozic C., van Dieren S., van Amerongen M. J., Chapelle T., Engelbrecht MRW., Gerhards M. F., Grunhagen D., van Gulik T. M., Hermans J. J., de Jong K. P., Klaase J. M., Liem MSL., van Lienden K. P., Molenaar I. Q., Patijn G. A., Rijken A. M., Ruers T. M., Verhoef C., de Wilt JHW., Marquering H. A., Stoker J., Swijnenburg R. J., Punt CJA., Huiskens J., Kazemier G. Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases. Eur Radiol Exp. 2023. 7(1), 75. doi: 10.1186/s41747-023-00383-4.
Xu Z., Jiang G., Dai J. Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects. Oncol Rev. 2024. 18, 1435922. doi: 10.3389/or.2024.1435922.
Yu S. H., Choi S. J., Noh H., Lee I. S., Park S. H., Kim S. J. Comparison of CT Volumetry and RECIST to Predict the Treatment Response and Overall Survival in Gastric Cancer Liver Metastases. Taehan Yongsang Uihakhoe Chi. 2021. 82(4), 876–888. doi: 10.3348/jksr.2020.0085.
Zane K. E., Cloyd J. M., Mumtaz K. S., Wadhwa V., Makary M. S. Metastatic disease to the liver: Locoregional therapy strategies and outcomes. World J Clin Oncol. 2021. 12(9), 725–745. doi: 10.5306/wjco.v12.i9.725.











