Introduction: The aim of this retrospective, observational, descriptive study was to identify predictors of response to neoadjuvant therapy in breast cancer patients and to validate them using three anatomopathological scores, such as residual cancer burden (RCB) score, Chevallier system, and tumor-infiltrating lymphocytes (TIL) score.
Materials And Methods: We conducted a study on 88 female patients aged 37 to 78 years with confirmed breast cancer who were indicated for neoadjuvant chemotherapy. We analyzed different individual variables (such as age, menarche, and menopause), clinical/imaging characteristics of the breast tumor and axillary nodes, immunohistochemical biomarkers (such as ER/PR/HER2 and Ki67), and histopathological features (such as subtype and grading) in relation to three anatomopathological scores calculated based on the surgical resection specimen.
Results: The percentage of patients who could have benefited from conservative surgery increased from 6% at the time of diagnosis to 20% post-primary systemic therapy (PST). Age under 49 (p = 0.01), premenopausal status (p < 0.01), no special type (NST) (p = 0.04), high Ki67 (p < 0.01), triple-negative breast cancer (TNBC) (p = 0.02) are positive predictive factors of neoadjuvant therapy, while lobular/mixt carcinoma-type (p = 0.04), luminal A (p = 0.01), positive lymph node (p < 0.01), and low differentiation grade (p = 0.01) are negative predictive factors for the response to PST.
Conclusion: There is a strong correlation between the RCB score and the Chevallier system for quantifying the response to PST, with most predictive factors being confirmed through appropriate statistical analysis for both. TIL score values correlated with only some of the predictors, most likely due to the importance of calculating this score on both biopsy specimens at diagnosis and resection specimens after chemotherapy.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11139452 | PMC |
| http://dx.doi.org/10.7759/cureus.59391 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival.
Sci Rep
December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
View Article and Find Full Text PDFAssessment of resilient modulus of soil using hybrid extreme gradient boosting models.
Sci Rep
December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
View Article and Find Full Text PDFWeb application using machine learning to predict cardiovascular disease and hypertension in mine workers.
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFPrognostic role of aetiological agent vs. clinical pattern in candidates to lead extraction for cardiac implantable electronic device infections.
Sci Rep
December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
View Article and Find Full Text PDFElucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.
Nat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!