Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach
Background: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-based tool to facilitate personalized treatment de...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Breast |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0960977625000074 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856928460111872 |
|---|---|
| author | Sakhr Alshwayyat Mahmoud Bashar Abu Al Hawa Mustafa Alshwayyat Tala Abdulsalam Alshwayyat Siya sawan Ghaith Heilat Hanan M. Hammouri Sara Mheid Batool Al Shweiat Hamdah Hanifa |
| author_facet | Sakhr Alshwayyat Mahmoud Bashar Abu Al Hawa Mustafa Alshwayyat Tala Abdulsalam Alshwayyat Siya sawan Ghaith Heilat Hanan M. Hammouri Sara Mheid Batool Al Shweiat Hamdah Hanifa |
| author_sort | Sakhr Alshwayyat |
| collection | DOAJ |
| description | Background: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-based tool to facilitate personalized treatment decisions. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. We also performed a Kaplan-Meier (K-M) survival analysis. Results: This study included 1212 patients. The median age was 60 years, with most tumors being localized and less than 2 cm in size. The 5-year overall survival (OS) rates were highest for surgery + radiotherapy (RT) (94.9 %) and lowest for surgery + chemotherapy (CTX) + RT (80.1 %). Positive estrogen receptor (ER) status and younger age were associated with better survival outcomes. ML models identified key predictive features for survival, including age, nodal status, and ER status. Conclusion: Age, lymph node metastasis, and ER status are crucial prognostic indicators for BACC. Although postoperative RT enhances survival, the advantages of adjuvant CTX are uncertain, implying that it may be eschewed to avert adverse effects. Our online tool offers essential resources for prognostication and treatment optimization. |
| format | Article |
| id | doaj-art-b5a99d2dfa514b31aed342e6ac4a9d99 |
| institution | Kabale University |
| issn | 1532-3080 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Breast |
| spelling | doaj-art-b5a99d2dfa514b31aed342e6ac4a9d992025-02-12T05:30:39ZengElsevierBreast1532-30802025-02-0179103878Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approachSakhr Alshwayyat0Mahmoud Bashar Abu Al Hawa1Mustafa Alshwayyat2Tala Abdulsalam Alshwayyat3Siya sawan4Ghaith Heilat5Hanan M. Hammouri6Sara Mheid7Batool Al Shweiat8Hamdah Hanifa9King Hussein Cancer Center, Amman, Jordan; Princess Basma Teaching Hospital, Irbid, Jordan; Research Fellow, Applied Science Research Center, Applied Science Private University, Amman, JordanFaculty of Medicine, Jordan University of Science & Technology, Irbid, JordanFaculty of Medicine, Jordan University of Science & Technology, Irbid, JordanPrincess Basma Teaching Hospital, Irbid, JordanFaculty of Medicine, University of Jordan, Amman, JordanBreast Oncoplastic and General Surgery, Department of General Surgery and Urology, Jordan University of Science & Technology, King Abdullah University Hospital, Irbid, JordanDepartment of Mathematics and Statistics, Faculty of Arts and Science, Jordan University of Science and Technology, Irbid, JordanRadiation Oncology Department, King Hussein Cancer Center, Amman, JordanBreast Imaging Fellow, Department of Radiology, King Hussein Cancer Center, Amman, JordanFaculty of Medicine, University of Kalamoon, Al_Nabk, Syria; Corresponding author.Background: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-based tool to facilitate personalized treatment decisions. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. We also performed a Kaplan-Meier (K-M) survival analysis. Results: This study included 1212 patients. The median age was 60 years, with most tumors being localized and less than 2 cm in size. The 5-year overall survival (OS) rates were highest for surgery + radiotherapy (RT) (94.9 %) and lowest for surgery + chemotherapy (CTX) + RT (80.1 %). Positive estrogen receptor (ER) status and younger age were associated with better survival outcomes. ML models identified key predictive features for survival, including age, nodal status, and ER status. Conclusion: Age, lymph node metastasis, and ER status are crucial prognostic indicators for BACC. Although postoperative RT enhances survival, the advantages of adjuvant CTX are uncertain, implying that it may be eschewed to avert adverse effects. Our online tool offers essential resources for prognostication and treatment optimization.http://www.sciencedirect.com/science/article/pii/S0960977625000074Adenoid cystic carcinomaBreast neoplasmsEstrogen receptorsMachine learningPrognosisSurvival analysis |
| spellingShingle | Sakhr Alshwayyat Mahmoud Bashar Abu Al Hawa Mustafa Alshwayyat Tala Abdulsalam Alshwayyat Siya sawan Ghaith Heilat Hanan M. Hammouri Sara Mheid Batool Al Shweiat Hamdah Hanifa Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach Breast Adenoid cystic carcinoma Breast neoplasms Estrogen receptors Machine learning Prognosis Survival analysis |
| title | Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach |
| title_full | Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach |
| title_fullStr | Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach |
| title_full_unstemmed | Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach |
| title_short | Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach |
| title_sort | personalized treatment strategies for breast adenoid cystic carcinoma a machine learning approach |
| topic | Adenoid cystic carcinoma Breast neoplasms Estrogen receptors Machine learning Prognosis Survival analysis |
| url | http://www.sciencedirect.com/science/article/pii/S0960977625000074 |
| work_keys_str_mv | AT sakhralshwayyat personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT mahmoudbasharabualhawa personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT mustafaalshwayyat personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT talaabdulsalamalshwayyat personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT siyasawan personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT ghaithheilat personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT hananmhammouri personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT saramheid personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT batoolalshweiat personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach AT hamdahhanifa personalizedtreatmentstrategiesforbreastadenoidcysticcarcinomaamachinelearningapproach |