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Alghafees M, Seyam RM, Al-Hussain T, Amin TM, Altaweel W, Sabbah BN, Sabbah AN, Almesned R, Alessa L. Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients. Urol Ann 2024; 16:94-97. [PMID: 38415235 PMCID: PMC10896329 DOI: 10.4103/ua.ua_32_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/24/2023] [Accepted: 10/13/2023] [Indexed: 02/29/2024] Open
Abstract
Objectives Gastrointestinal stromal tumors (GISTs) can occur synchronously with other neoplasms, including the genitourinary (GU) system. Machine learning (ML) may be a valuable tool in predicting synchronous GU tumors in GIST patients, and thus improving prognosis. This study aims to evaluate the use of ML algorithms to predict synchronous GU tumors among GIST patients in a specialist research center in Saudi Arabia. Materials and Methods We analyzed data from all patients with histopathologically confirmed GIST at our facility from 2003 to 2020. Patient files were reviewed for the presence of renal cell carcinoma, adrenal tumors, or other GU cancers. Three supervised ML algorithms were used: logistic regression, XGBoost Regressor, and random forests (RFs). A set of variables, including independent attributes, was entered into the models. Results A total of 170 patients were included in the study, with 58.8% (n = 100) being male. The median age was 57 (range: 9-91) years. The majority of GISTs were gastric (60%, n = 102) with a spindle cell histology. The most common stage at diagnosis was T2 (27.6%, n = 47) and N0 (20%, n = 34). Six patients (3.5%) had synchronous GU tumors. The RF model achieved the highest accuracy with 97.1%. Conclusion Our study suggests that the RF model is an effective tool for predicting synchronous GU tumors in GIST patients. Larger multicenter studies, utilizing more powerful algorithms such as deep learning and other artificial intelligence subsets, are necessary to further refine and improve these predictions.
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Affiliation(s)
- Mohammad Alghafees
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Raouf M Seyam
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Turki Al-Hussain
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Tarek Mahmoud Amin
- Department of Surgical Oncology, Oncology Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Waleed Altaweel
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | | | | | - Razan Almesned
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Laila Alessa
- Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
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Behl T, Kumar A, Vishakha, Sehgal A, Singh S, Sharma N, Yadav S, Rashid S, Ali N, Ahmed AS, Vargas-De-La-Cruz C, Bungau SG, Khan H. Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer. Int J Biol Macromol 2023; 253:126595. [PMID: 37648139 DOI: 10.1016/j.ijbiomac.2023.126595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/01/2023]
Abstract
Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.
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Affiliation(s)
- Tapan Behl
- School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
| | - Ankush Kumar
- Institute of Pharmaceutical Sciences, IET Bhaddal Technical Campus, Ropar 140108, Punjab, India
| | - Vishakha
- Institute of Pharmaceutical Sciences, IET Bhaddal Technical Campus, Ropar 140108, Punjab, India
| | - Aayush Sehgal
- GHG Khalsa College of Pharmacy, Gurusar Sadhar, 141104 Ludhiana, Punjab, India
| | - Sukhbir Singh
- Department of Pharmaceutics, MM College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana Ambala 133203, Haryana, India
| | - Neelam Sharma
- Department of Pharmaceutics, MM College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana Ambala 133203, Haryana, India
| | - Shivam Yadav
- School of Pharmacy, Babu Banarasi Das University, Lucknow 226028, Uttar Pradesh, India
| | - Summya Rashid
- Department of Pharmacology and Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia.
| | - Nemat Ali
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadah 11451, Saudi Arabia
| | - Amira Saber Ahmed
- Hormones Department, Medical Research and Clinical Studies Institute, National Research Centre, Giza 12622, Egypt
| | - Celia Vargas-De-La-Cruz
- Department of Pharmacology, Bromatology and Toxicology, Faculty of Pharmacy and Biochemistry, Universidad Nacional Mayor de San Marcos, Lima 150001, Peru; E-Health Research Center, Universidad de Ciencias y Humanidades, Lima 15001, Peru
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, Oradea 410087, Romania; Doctoral School of Biomedical Sciences, University of Oradea, Oradea 410087, Romania
| | - Haroon Khan
- Department of Pharmacy, Abdul Wali Khan University, Mardan 23200, Pakistan.
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van den Ende NS, Nguyen AH, Jager A, Kok M, Debets R, van Deurzen CHM. Triple-Negative Breast Cancer and Predictive Markers of Response to Neoadjuvant Chemotherapy: A Systematic Review. Int J Mol Sci 2023; 24:ijms24032969. [PMID: 36769287 PMCID: PMC9918290 DOI: 10.3390/ijms24032969] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Around 40-50% of all triple-negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) after treatment with neoadjuvant chemotherapy (NAC). The identification of biomarkers predicting the response to NAC could be helpful for personalized treatment. This systematic review provides an overview of putative biomarkers at baseline that are predictive for a pCR following NAC. Embase, Medline and Web of Science were searched for articles published between January 2010 and August 2022. The articles had to meet the following criteria: patients with primary invasive TNBC without distant metastases and patients must have received NAC. In total, 2045 articles were screened by two reviewers resulting in the inclusion of 92 articles. Overall, the most frequently reported biomarkers associated with a pCR were a high expression of Ki-67, an expression of PD-L1 and the abundance of tumor-infiltrating lymphocytes, particularly CD8+ T cells, and corresponding immune gene signatures. In addition, our review reveals proteomic, genomic and transcriptomic markers that relate to cancer cells, the tumor microenvironment and the peripheral blood, which also affect chemo-sensitivity. We conclude that a prediction model based on a combination of tumor and immune markers is likely to better stratify TNBC patients with respect to NAC response.
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Affiliation(s)
- Nadine S. van den Ende
- Department of Pathology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, 3015 GD Rotterdam, The Netherlands
- Correspondence: ; Tel.: +31-640213383
| | - Anh H. Nguyen
- Department of Pathology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, 3015 GD Rotterdam, The Netherlands
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, 3015 GD Rotterdam, The Netherlands
| | - Marleen Kok
- Department of Medical Oncology, Tumor Biology & Immunology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Reno Debets
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, 3015 GD Rotterdam, The Netherlands
| | - Carolien H. M. van Deurzen
- Department of Pathology, Erasmus MC Cancer Institute, Erasmus University Medical Centre, 3015 GD Rotterdam, The Netherlands
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