Cargando…
Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy
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 predic...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017935/ https://www.ncbi.nlm.nih.gov/pubmed/36936199 http://dx.doi.org/10.1177/17588359221138657 |
_version_ | 1784907700194246656 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Garufi, Giovanna |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10017935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-100179352023-03-17 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 Ther Adv Med Oncol Original Research 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. SAGE Publications 2023-03-14 /pmc/articles/PMC10017935/ /pubmed/36936199 http://dx.doi.org/10.1177/17588359221138657 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research 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 Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy |
title | Development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
title_full | Development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
title_fullStr | Development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
title_full_unstemmed | Development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
title_short | Development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
title_sort | development of a nomogram for predicting pathological complete
response in luminal breast cancer patients following neoadjuvant
chemotherapy |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017935/ https://www.ncbi.nlm.nih.gov/pubmed/36936199 http://dx.doi.org/10.1177/17588359221138657 |
work_keys_str_mv | AT garufigiovanna developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT carbogninluisa developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT sperdutiisabella developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT migliettafederica developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT diecimariavittoria developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT mazzeoroberta developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT orlandiarmando developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT gerratanalorenzo developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT palazzoantonella developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT fabialessandra developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT parisida developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT francoantonio developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT franceschinigianluca developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT fiorioelena developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT pilottosara developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT guarnerivalentina developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT puglisifabio developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT contepierfranco developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT milellamichele developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT scambiagiovanni developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT tortoragiampaolo developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy AT briaemilio developmentofanomogramforpredictingpathologicalcompleteresponseinluminalbreastcancerpatientsfollowingneoadjuvantchemotherapy |