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MR tomographic evaluation of the effectiveness of neoadjuvant chemotherapy for breast cancer based on pharmacokinetic numerical analysis of tumor uptake of paramagnetic contrast in intravenous contrast enhancement

https://doi.org/10.18705/2311-4495-2024-11-5-428-444

EDN: ERFXXC

Abstract

Background. In clinical practice, MRI pathophysiological and pharmacokinetic models with calculations of contrast transport indicators are unacceptably little used.

Aim of the study. To propose a pharmacokinetic technique for the quantitative assessment of primary tumors and metastases, the effectiveness of breast cancer chemotherapy (BC), from dynamic contrast enhancement MRI. Material and methods. 18 patients were included who underwent neoadjuvant chemotherapy (NACHT) in four cycles for breast cancer T1-3N0-1M0, followed by radical surgical removal of the tumor. According to the results of a three — year follow-up, patients with a relapse — free course (n = 11) formed group 1, and seven patients with detected metastatic lesions (3 in the liver, 2 in the lungs, 2 in the brain) — group 2. For the initial 120–180 s of MRI with contrast after the injection of a paramagnetic, the simplification is valid for concentrations in tumor and blood and for tranfer coefficient: d{СОПУХОЛЬ(t)} / dt = ККр-Оп * СКРОВЬ (t) , from which it is obvious: ККр-Оп = (СОПУХОЛЬ(Т)) / (∫CКРОВЬ(t) dt), which was used in all calculations of the contrast transfer constant. Gadobutrol contrast is 0.1M/10 kg of body weight, TR = 5.5–6 ms, TE = 2.5 ms. Results. If, after the first cycle of NACHT, the ККр-Оп of the primary breast cancer is < 0.22 ml/min/g of tissue, the probability of subsequent relapse-free course = 0.78. Similarly, a decrease in the KCr-Op Ккр-Оп of the sentinel lymph node after the first cycle of NAHT < 0.08 ml/min/g of tissue with a probability of 0.75 predicts a relapse-free course. Conclusion. The proposed method for calculating the ККр-Оп transfer coefficient allows obtaining additional diagnostic and prognostic information in patients with local and locally advanced forms of breast cancer.

About the Authors

W. Yu. Ussov
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Wladimir Yu. Ussov, Doctor of Medical Sciences, Professor, Chief Researcher of the Department of Radiation and Functional Research Methods

Rechkunovskaya str., 15, Novosibirsk, 630055

usov_v@meshalkin.ru


Competing Interests:

The authors declare no conflict of interest.



S. M. Minin
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Stanislav M. Minin, Candidate of Medical Sciences, Researcher of the Research Department of Oncology and Radiotherapy of the Institute of Oncology and Neurosurgery

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



N. A. Nikitin
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Nikita A. Nikitin, head of the X-ray department

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



E. N. Nogina
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Elena N. Nogina, Head of the Department of Drug and Antitumor Therapy at the Institute of Oncology and Neurosurgery

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



I. A. Kosarev
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Ilya A. Kosarev, oncologist-mammologist at the Institute of Oncology and Neurosurgery

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



E. Kobelev
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Evgeny Kobelev, Candidate of Medical Sciences, Junior Researcher at the Scientific Research Department of Radiation and Instrumental Research Methods

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



L. V. Bashkirov
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Leonid V. Bashkirov, is a junior researcher at the Scientific Research Department of Radiation and Instrumental Research Methods

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



Zh. Zh. Anashbaev
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Zhanat Zh. Anashbaev, radiologist of the Radiotherapy Department

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



N. A. Tarabanovskaya
Research Institute of Oncology of the Tomsk Scientific Research Center of the Russian Academy of Sciences
Russian Federation

Natalia A. Tarabanovskaya, Candidate of Medical Sciences, Researcher, surgeon of the highest category of the Department of General Oncology

Tomsk


Competing Interests:

The authors declare no conflict of interest.



V. Yu. Babikov
Research Institute of Pharmacology and Restorative Medicine of the Tomsk Scientific Research Center of the Russian Academy of Sciences
Russian Federation

Viktor Yu. Babikov, is a postgraduate student at the E. D. Goldberg Research Institute of Pharmacology and Regenerative Medicine

Tomsk


Competing Interests:

The authors declare no conflict of interest.



Na. V. Denisova
National Research Novosibirsk State University ; Institute of Theoretical and Applied Mechanics named after Academician S. A. Khristianovich, Siberian Branch of the Russian Academy of Sciences
Russian Federation

Natalia V. Denisova, Doctor of Physical and Mathematical Sciences, Professor, leading researcher

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



A. L. Chernyshova
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Alyona L. Chernyshova, Doctor of Medical Sciences, Professor of the Russian Academy of Sciences, Director of the Institute of Oncology and Neurosurgery

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



A. M. Chernyavsky
National Medical Research Center named after Academician E. N. Meshalkin
Russian Federation

Aleksandr M. Cherniavskii, Doctor of Medical Sciences, Professor, Corresponding Member of the Russian Academy of Sciences, General Director

Novosibirsk


Competing Interests:

The authors declare no conflict of interest.



References

1. Shirshin AV, Boikov IV, Malakhovsky VN, et al. Application of digital processing methods for automated segmentation of the heart according to computed tomography data // Proceedings of the Russian Military Medical Academy. 2022; 41:1:49–54. In Russian [ DOI 10.17816/rmmar104344.

2. Varfolomeev SD, Gurevich KG. Biokinetics. A practical course. Moscow. FAIR PRESS. 1999; 720. In Russian

3. Wang W, Lv S, Xun J, et al. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer. Eur J Radiol. 2022; 154:110392. DOI: 10.1016/j.ejrad.2022.110392.

4. Li X, Rooney WD, Springer CS. A unified magnetic resonance imaging pharmacokinetic theory: intravascular and extracellular contrast reagents. Magn Reson Med. 2005; 54:6: 1351–9. DOI: 10.1002/mrm.20684.

5. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017; 18:3: e143–e152. DOI: 10.1016/S1470-2045(17)30074-8.

6. Serebryakova SV, Shumakova TA, Safronova OB, et al. Magnetic resonance mammography in the diagnosis of intraductal cancer in situ (DCIS)// Radiology — practice. 2021; 3:87:41–61. In Russian DOI 10.52560/2713-0118-2021-3-41-61.

7. Chipiga LA, Vodovatov AV, Kataeva GV, et al. Proposals of quality assurance in positron emission tomography in Russia. Medical Physics. 2019; 2:82:78–92. In Russian

8. Nenakhova YuN. Radiological early predictors of response to neoadjuvant chemotherapy in breast cancer patients. // Diagnostic and interventional radiology. 2017; 11:1:67–73. In Russian DOI: 10.25512/DIR.2017.11.1.09.

9. Ioannidis GS, Maris TG, Nikiforaki K, et al. Investigating the Correlation of Ktrans With SemiQuantitative MRI Parameters Towards More Robust and Reproducible Perfusion Imaging Biomarkers in Three Cancer Types. IEEE J Biomed Health Inform. 2019; 23:5:1855–1862. DOI: 10.1109/JBHI.2018.2888979.

10. Thawani R, Gao L, Mohinani A, et al. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study. BMC Med Imaging. 2022; 22:1:182. DOI: 10.1186/s12880-022-00908-0.

11. Ussov WYu, Ryannel YuE, Popadich S, et al. The possibilities of single-photon emission computed tomography with 99mTc-Technetril in the diagnosis and assessment of anatomic extent of breast cancer. Medical vizualisation. 2001; 4(3):74–86. In Russian

12. Ussov WYu, Ryannel YuE, Medvedeva AA, et al. Mammoscintigraphy with 99mTc-Technetril in assessing the state of a primary tumor in breast cancer chemotherapy// Medical vizualisation. 2002; 5:2:86–94. In Russian

13. Krzhivitsky PI, Novikov SN, Kanaev SV, et al. SPECT-CT diagnosis od metastatic lymph nodes in breast cancer patients. Problems in onclology. 2017; 63:2:261–266. In Russian

14. Semiglazov VF, Gorbunova VA, Tyulyandin SA. Chemotherapy of breast cancer: a modern view of the problem. Medical Council. 2017; 6:56–60. In Russian DOI 10.21518/2079-701X-2017-6-56-60.

15. Tudorica A, Oh KY, Chui SY-C, et al. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCEMRI. Transl Oncol. 2016; 9:1:8–17. DOI: 10.1016/j.tranon.2015.11.016.

16. Liang X, Chen X, Yang Z, et al. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer. BMC Cancer. 2022; 22:1:1250. DOI: 10.1186/s12885-022-10315-x.

17. Wang TC, Huang YH, Huang CS, et al. Computeraided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magn Reson Imaging. 2014; 32:3:197–205. DOI: 10.1016/j.mri.2013.12.002.

18. MRI with dynamic contrast enhancement in the assessment of axillary lymph nodes in breast cancer after neoadjuvant chemotherapy. P. Y. Grishko, A. S. Petrova, A. V. Komyakhov, A. V. Mishchenko. Collection of scientific papers of the III St. Petersburg International Oncological Forum “White Nights 2017”, St. Petersburg, June 22–24, 2017. Federal State Budgetary Institution “N. N. Petrov Research Institute of Oncology” of the Ministry of Health of the Russian Federation. St. Petersburg: “Problems of Oncology”. 2017. In Russian


Review

For citations:


Ussov W.Yu., Minin S.M., Nikitin N.A., Nogina E.N., Kosarev I.A., Kobelev E., Bashkirov L.V., Anashbaev Zh.Zh., Tarabanovskaya N.A., Babikov V.Yu., Denisova N.V., Chernyshova A.L., Chernyavsky A.M. MR tomographic evaluation of the effectiveness of neoadjuvant chemotherapy for breast cancer based on pharmacokinetic numerical analysis of tumor uptake of paramagnetic contrast in intravenous contrast enhancement. Translational Medicine. 2024;11(5):428-444. (In Russ.) https://doi.org/10.18705/2311-4495-2024-11-5-428-444. EDN: ERFXXC

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ISSN 2311-4495 (Print)
ISSN 2410-5155 (Online)