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Abstract
Aim. To determine the most informative method of radiodiagnosis and the capabilities of a neural network in assessing the response to neoadjuvant treatment of the most aggressive molecular subtypes of breast cancer by comparing it with pathomorphological data.
Material and Methods. The material for the study was medical documentation data (medical histories and outpatient records) of 336 breast cancer patients who underwent examination and treatment at the State Autonomous Health Care Institution of the Sverdlovsk Region «Sverdlovsk Regional Oncology Centre», (Yekaterinburg) in 2021-2022; the average age of the patients was 57.6 (± 10.3) years. The trial enrolled patients with operable and locally advanced tumors (cT1N1, cT2N1, cT2-3N0-1) of various IHC subtypes who required neoadjuvant chemotherapy (NACT) to reduce tumor mass. Response to drug therapy was assessed using RECIST 1.1 criteria. (Response Evaluation Criteria in Solid Tumors). Histological specimens were examined before and after surgery to determine residual tumor or pathological complete response (pCR). All patients underwent mammography, ultrasound, MRI and NS imaging data were analyzed. A comparison of radiographic (rCR) and pathological (pCR) tumor response was presented.
Results. Breast tumor regression according to histological examination (pCR) was detected in 34.5 % (n = 116) of cases. A complete tumor response to NAC was achieved in 44.8 % of cases (n = 52) in the luminal B/HER2+ subtype, while in the non-luminal/HER2+ subtype only 37.9 % (n = 44) of women had a pCR. In triple-negative breast cancer, only 17.2 % (n = 20) of cases had no histological evidence of the tumor. Complete regression according to radiological research methods (rCR) was detected by mammography in 28.6 % (n = 96 people), by ultrasound in 29.8 % (n = 100 people), by MRI in 32.1 % (n = 108 people), the neural network detected complete tumor regression in 23.8 % of cases (n = 80). MRI demonstrated the highest sensitivity in detecting residual tumor (80.0-83.3 %), depending on the molecular subtype.
The neural network has proven to be comparable to mammography in terms of sensitivity of 69.2-72.0 %, depending on the biological characteristics of the tumour, and specificity of 60.0-62.2 %.
Conclusion. The high effectiveness of radiation methods in multimodal diagnostics in assessing and predicting tumor response to NAC has been proven. The trained neural network model has demonstrated the ability to detect residual tumor at the mammographic level.
References
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