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Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy.
Garufi, Giovanna; Carbognin, Luisa; Sperduti, Isabella; Miglietta, Federica; Dieci, Maria Vittoria; Mazzeo, Roberta; Orlandi, Armando; Gerratana, Lorenzo; Palazzo, Antonella; Fabi, Alessandra; Paris, Ida; Franco, Antonio; Franceschini, Gianluca; Fiorio, Elena; Pilotto, Sara; Guarneri, Valentina; Puglisi, Fabio; Conte, Pierfranco; Milella, Michele; Scambia, Giovanni; Tortora, Giampaolo; Bria, Emilio.
Afiliação
  • Garufi G; Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Carbognin L; Section of Medical Oncology, Università Cattolica del Sacro Cuore, Rome, Italy.
  • Sperduti I; Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, P.le A. Gemelli, Rome, 00168, Italy.
  • Miglietta F; Biostatistics, Regina Elena National Cancer Institute, IRCCS, Rome, Italy.
  • Dieci MV; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
  • Mazzeo R; Medical Oncology 2, Istituto Oncologico Veneto IOV-IRCCS, Padova, Italy.
  • Orlandi A; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
  • Gerratana L; Medical Oncology 2, Istituto Oncologico Veneto IOV-IRCCS, Padova, Italy.
  • Palazzo A; Oncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of Udine, Italy.
  • Fabi A; Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Paris I; Oncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of Udine, Italy.
  • Franco A; Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Franceschini G; Unit of Precision Medicine in Senology, Scientific Directorate, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Fiorio E; Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
  • Pilotto S; Breast Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
  • Guarneri V; Breast Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
  • Puglisi F; Medical Oncology, Department of Medicine, University of Verona Hospital Trust, Verona, Italy.
  • Conte P; Medical Oncology, Department of Medicine, University of Verona Hospital Trust, Verona, Italy.
  • Milella M; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
  • Scambia G; Medical Oncology 2, Istituto Oncologico Veneto IOV-IRCCS, Padova, Italy.
  • Tortora G; Oncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of Udine, Italy.
  • Bria E; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
Ther Adv Med Oncol ; 15: 17588359221138657, 2023.
Article em En | MEDLINE | ID: mdl-36936199
ABSTRACT

Background:

Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features.

Methods:

Clinicopathological data from stage I-III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated.

Results:

In the training set, data from 539 patients were gathered pCR rate was 11.3% [95% confidence interval (CI) 8.6-13.9] (luminal A-like 5.3%, 95% CI 1.5-9.1, and luminal B-like 13.1%, 95% CI 9.8-13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC) 0.69; p < 0.001). Clinical stage I-II (OR 3.67, 95% CI 1.75-7.71, p = 0.001), Ki67 ⩾44% (OR 3.00, 95% CI 1.59-5.65, p = 0.001), and progesterone receptor (PR) <1% (OR 2.49, 95% CI 1.15-5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I-II, PR < 1%, and Ki67 ⩾44% (accuracy AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC 0.7; p < 0.0001).

Conclusion:

The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ther Adv Med Oncol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Itália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ther Adv Med Oncol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Itália