ArticleMedeniyet medical journal2023
Does Apoptotic Index Predict the Response to Neoadjuvant Chemotherapy in Patients with Breast Carcinoma?
Article in Medeniyet medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Neoadjuvant chemotherapy (NACT) plays a major role in the treatment of patients with locally advanced breast carcinoma. Although most patients have benefited from NACT, the rate of residual tumors is still high after treatment (AT). An increase in apoptosis is expected in tru-cut biopsy (TCB) during treatment or AT as the mechanism of NACT is inducing apoptosis. This study aimed to investigate whether evaluating the apoptotic index (AI) from TCB can predict the response before treatment (TC-BT) and whether there is a correlation between AI and clinicopathologic parameters. Methods: Seventy cases of breast carcinomas were included. The AI was evaluated BT and AT by quantifying the apoptosis. The receiver operating characteristic analysis was performed with overall survival (OS) data, and low and high AI cut-offs were obtained. The relationship between AI and response and clinicopathological parameters was evaluated. Results: A significant relationship was found between low AI in TC-BT and at least partial response (p=0.025), longer OS (p=0.01) and disease-free survival (p=0.01), and progesterone receptor-positive tumors (p=0.03). Her2-negative tumors were more prone to low AI. A significant decline in AI (p=0.001) and Ki67 proliferation index (p<0.001) was observed in resections AT. Conclusions: These data suggested that the AI is a simple and cost-effective tool that may play an important role in determining response, and a low AI in TC-BT may have some value as a predictive marker in breast carcinomas.
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