prikaz prve stranice dokumenta Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment
  Preuzmi
PDF 3.26 MB
Znanstveni rad - Izvorni znanstveni rad
Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment
Cancers, 15 (2023), 3; 634. https://doi.org/10.3390/cancers15030634
Vrdoljak, Josip; Boban, Zvonimir; Barić, Domjan; Šegvić, Darko; Kumrić, Marko; Avirović, Manuela; Perić Balja, Melita; Periša, Marija Milković; Tomasović, Čedna; Tomić, Snježana; Vrdoljak, Eduard; Božić, Joško

Institucijski repozitorij: Repozitorij Prirodoslovno-matematičkog fakulteta

Citirajte ovaj rad

Vrdoljak, J., Boban, Z., Barić, D., Šegvić, D., Kumrić, M., Avirović, M. ... Božić, J. (2023). Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment. Cancers, 15. (3). doi: 10.3390/cancers15030634

Vrdoljak, Josip, et al. "Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment." Cancers, vol. 15, br. 3, 2023. https://doi.org/10.3390/cancers15030634

Vrdoljak, Josip, Zvonimir Boban, Domjan Barić, Darko Šegvić, Marko Kumrić, Manuela Avirović, Melita Perić Balja, et al. "Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment." Cancers 15, br. 3 (2023). https://doi.org/10.3390/cancers15030634

Vrdoljak, J., et al. (2023) 'Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment', Cancers, 15(3). doi: 10.3390/cancers15030634

Vrdoljak J, Boban Z, Barić D, Šegvić D, Kumrić M, Avirović M, i sur.. Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment. Cancers [Internet]. 19.01.2023. [pristupljeno 10.05.2024.];15(3). doi: 10.3390/cancers15030634

J. Vrdoljak, et al., "Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment", Cancers, vol. 15, br. 3, Siječanj 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:217:451410. [Citirano: 10.05.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:217:451410
Podaci o radu
Naslov (engleski)Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment
AutorJosip Vrdoljak
AutorZvonimir Boban
AutorDomjan Barić
AutorDarko Šegvić
AutorMarko Kumrić
AutorManuela Avirović
AutorMelita Perić Balja
AutorMarija Milković Periša
AutorČedna Tomasović
AutorSnježana Tomić
AutorEduard Vrdoljak
AutorJoško Božić
Autorova ustanovaSveučilište u Zagrebu
Prirodoslovno-matematički fakultet
(Fizički odsjek)
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Interdisciplinarne prirodne znanosti
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Sažetak (engleski)
Background: Due to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction. Methods: From the total breast cancer patient data (n = 8381), 719 patients were identified as eligible for NST. Machine learning models were applied for the NST-criteria group and the total study population. Model explainability was obtained by calculating Shapley values. Results: In the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors. Conclusion: Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaCancers
Brojčani podacivol. 15, br. 3, 634
e-ISSN2072-6694
DOIhttps://doi.org/10.3390/cancers15030634
URN:NBNurn:nbn:hr:217:451410
Datum objave publikacije2023-01-19
URL dokumentahttps://www.mdpi.com/2072-6694/15/3/634
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Javna napomenaCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Received: 14 December 2022; Revised: 16 January 2023; Accepted: 17 January 2023; Published: 19 January 2023.
Datum i vrijeme pohrane2023-08-21 12:45:36