Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

ÇOLAK, Cemil; YAĞIN, Fatma Hilal; Akbulut, Sami

doi:10.4274/imj.galenos.2022.62443

Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

AHMET SAMİ AKBULUT, (İnönü Üniversitesi Tıp Fakültesi, Cerrahi, Biyoistatistik ve Tıp Bilişimi ve Halk Sağlığı Anabilim Dalı, Malatya, Türkiye)

Fatma Hilal YAĞIN, (İnönü Üniversitesi Tıp Fakültesi Biyoistatistik ve Tıp Bilişimi Anabilim Dalı Malatya, Türkiye)

CEMİL ÇOLAK (İnönü Üniversitesi Tıp Fakültesi Biyoistatistik ve Tıp Bilişimi Anabilim Dalı Malatya, Türkiye)

İstanbul Medical Journal

13 0

Yıl: 2022 Cilt: 23 Sayı: 3 Sayfa Aralığı: 210 - 215 Metin Dili: İngilizce DOI: 10.4274/imj.galenos.2022.62443 İndeks Tarihi: 22-09-2022

Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

Öz:

Introduction: Despite significant advances in breast cancer (BC) management, the prognosis for most patients with distant metastasis remains poor. We predicted distant metastasis in BC patients with artificial intelligence (AI) methods based on genomic biomarkers. Methods: The dataset used in the study included 97 patients with BC, of whom 46 (47%) developed distant metastases, and 51 (53%) did not develop distant metastases, and the expression level of 24,481 genes of these patients. An approach combining Boruta + LASSO methods was applied to identify biomarker genes associated with BC distant metastasis. Mann-Whitney U test was used to examine the difference between groups in terms of gene expression levels in statistical analyses, and Cohen d effect sizes and odds ratios were calculated. AdaBoost and XGBoost algorithms, which are tree-based methods, were used for BC distant metastasis prediction, and the results were compared by evaluating comprehensive performance criteria. Results: After Boruta + LASSO methods, 14 biomarker candidate genes were identified. These predictive genes were PIB5PA, SSX2, OR1F1, ALDH4A1, FGF18, WISP1, PRAME, CEGP1, AL080059, NMU, ATP5E, SMARCE1, FGD6, and SLC37A1. In effect size results; in particular, show that the AL080059 (Cohen’s D: 1.318) gene is clinically predictive of BC Metastasis. The accuracy, F1-score, positive predictive value, sensitivity, and area under the ROC Curve (AUC) values obtained with the AdaBoost algorithm for BC metastasis prediction was 95%, 96.3%, 100%, 92.6%, and 98.8%, respectively. The model created with the XGBoost algorithm, on the other hand, obtained 90%, 92.9%, 92.9%, 92.9%, 97.6% accuracy, F1-score, positive predictive value, sensitivity, and AUC values, respectively. Conclusion: Identifying genes that successfully predict BC distant metastasis with AI methods in the study may be decisive for future therapeutic targets and help clinicians better adapt adjuvant chemotherapy to their patients. Additionally, the AdaBoost prediction model created can discriminate patients at risk of BC distant metastases.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Akbulut S, YAĞIN F, ÇOLAK C (2022). Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. , 210 - 215. 10.4274/imj.galenos.2022.62443
Chicago	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. (2022): 210 - 215. 10.4274/imj.galenos.2022.62443
MLA	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. , 2022, ss.210 - 215. 10.4274/imj.galenos.2022.62443
AMA	Akbulut S,YAĞIN F,ÇOLAK C Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. . 2022; 210 - 215. 10.4274/imj.galenos.2022.62443
Vancouver	Akbulut S,YAĞIN F,ÇOLAK C Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. . 2022; 210 - 215. 10.4274/imj.galenos.2022.62443
IEEE	Akbulut S,YAĞIN F,ÇOLAK C "Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective." , ss.210 - 215, 2022. 10.4274/imj.galenos.2022.62443
ISNAD	Akbulut, Sami vd. "Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective". (2022), 210-215. https://doi.org/10.4274/imj.galenos.2022.62443

APA	Akbulut S, YAĞIN F, ÇOLAK C (2022). Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. İstanbul Medical Journal, 23(3), 210 - 215. 10.4274/imj.galenos.2022.62443
Chicago	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. İstanbul Medical Journal 23, no.3 (2022): 210 - 215. 10.4274/imj.galenos.2022.62443
MLA	Akbulut Sami,YAĞIN Fatma Hilal,ÇOLAK Cemil Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. İstanbul Medical Journal, vol.23, no.3, 2022, ss.210 - 215. 10.4274/imj.galenos.2022.62443
AMA	Akbulut S,YAĞIN F,ÇOLAK C Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. İstanbul Medical Journal. 2022; 23(3): 210 - 215. 10.4274/imj.galenos.2022.62443
Vancouver	Akbulut S,YAĞIN F,ÇOLAK C Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective. İstanbul Medical Journal. 2022; 23(3): 210 - 215. 10.4274/imj.galenos.2022.62443
IEEE	Akbulut S,YAĞIN F,ÇOLAK C "Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective." İstanbul Medical Journal, 23, ss.210 - 215, 2022. 10.4274/imj.galenos.2022.62443
ISNAD	Akbulut, Sami vd. "Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective". İstanbul Medical Journal 23/3 (2022), 210-215. https://doi.org/10.4274/imj.galenos.2022.62443