Abstract
Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) quantitative imaging (radiomics) approaches to predict the outcome of radio-immunotherapy treatments in a syngeneic subcutaneous murine breast tumor model. Five Athymic Nude mice were implanted with breast cancer cell lines (4T1) tumors on the right flank. The animals were CT- and MRI-imaged, tumors were contoured, and radiomics features were extracted. All animals were treated with radiotherapy (RT), followed by the administration of PD1 inhibitor. Approximately 10 days later, the animals were sacrificed, tumor volumes were measured, and histopathology evaluation was performed through Ki-67 staining. Linear regression modeling between radiomics and Ki-67 results was performed to establish a correlation between quantitative imaging and post-treatment histochemistry. There was no correlation between tumor volumes and Ki-67 values. Multiple CT- and MRI-derived features, however, correlated with histopathology with correlation coefficients greater than 0.8. MRI imaging helps in tumor delineation as well as an additional orthogonal imaging modality for quantitative imaging purposes. This is the first investigation correlating simultaneously CT- and MRI-derived radiomics to histopathology outcomes of combined radio-immunotherapy treatments in a preclinical setting applied to treatment naïve tumors. The findings indicate that imaging can guide discrimination between responding and non-responding tumors for the combined RT and ImT treatment regimen in TNBC.
Highlights
Triple-negative breast cancer (TNBC) represents approximately 15–20% of all newly-diagnosed breast cancers, and it remains the most aggressive subtype with the poorest outcome [1]
The 95% confidence interval for the slope and the intercept in the MRI equation are −0.275 to −0.168 and 0.178 to 0.265, respectively. These results indicate that the third-order joint probability texture features, represented by the co-occurrence cubes, are best suited as predictors in the multiple regression analyses among imaging variables and post-therapy histopathology tumor proliferation markers in both computed tomography (CT) and MRI
This work is the first demonstration of CT and MRI quantitative imaging application combined to radio-immunotherapy treatments in the treatment of naïve breast tumors, where the outcome is evaluated by histopathology
Summary
Triple-negative breast cancer (TNBC) represents approximately 15–20% of all newly-diagnosed breast cancers, and it remains the most aggressive subtype with the poorest outcome [1]. No molecular targets exist for TNBC, and alternative treatment strategies are required. Radiotherapy (RT) is a standard-of-care treatment and RT-induced DNA damage causes direct tumor cell death. RT induces immune responses through the release of tumor antigens and the generation of a. Sci. 2020, 10, 6493 favorable inflammatory cytokine [2]. Immune-checkpoint inhibitors (immunotherapy, or ImT) can augment RT effects by increasing tumor-specific T-cell proliferation, thereby improving outcomes over
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