부산대학교 도서관

Simple Summary: Nowadays patients affected by locally advanced breast cancer and particular subtypes of early breast cancer may benefit from neoadjuvant chemotherapy (NAC) before surgery with different advantageous, like reduction of tumor size and prognosis improvement. Pathological complete response to NAC is very variable amongst all different histological and immunophenotypic subtypes of breast cancer and its correct assessment by imaging is crucial for treatment planning, as patients could be addressed to conservative or demolitive breast surgery with reconstruction. Advanced imaging techniques, such as MRI and nuclear medicine, recently contributed to the prediction of chemotherapy response in the early phase of NAC, to avoid side effects and psychological implications of the oncological treatment in patients who are supposed to be unresponsive. This review article aims to compare different imaging techniques for both assessment and prediction of response to NAC and explain the new revolutionary contribute offered by Artificial Intelligence in this field. Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed. [ABSTRACT FROM AUTHOR]