Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer

Park, Seongyong; Yi, Gwan‐Su

doi:10.3390/cancers14040881

Cited by 8 publications

(2 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, ML models have recently been the subject of numerous research studies across various cancer types, including ovarian, thyroid, and breast cancer. [ 11 12 13 ] These studies demonstrate the potential of ML in predicting disease outcomes and identifying biomarkers for early diagnosis. Toth et al .…”

Section: Discussionmentioning

confidence: 88%

Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients

Alghafees,

Seyam,

Al-Hussain

et al. 2024

Urology Annals

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 88%

Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients

Alghafees,

Seyam,

Al-Hussain

et al. 2024

Urology Annals

View full text Add to dashboard Cite

show abstract

“…Random forests can be used in the identification of putative disease‐related biomarkers. In a study, researchers employed Random Forest to find genes that were typically observed in individuals with breast cancer (Park & Yi, 2022). The algorithm discovered a group of genes linked to cancer development that may be employed as biomarkers for early identification and individualized therapy.…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

Advancements in medical diagnosis and treatment through machine learning: A review

Ahsan,

Khan,

Khan

et al. 2023

Expert Systems

View full text Add to dashboard Cite

The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

show abstract

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures

Sasagawa,

Honma,

Peng

et al. 2024

Gastric Cancer

View full text Add to dashboard Cite

Background Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data. Methods We analyzed whole-genome and RNA sequences from biopsy specimens of 65 advanced gastric cancer patients before their chemotherapy treatment. Using machine learning techniques, we developed a model with 123 omics features, such as immune signatures and copy number variations, to predict their chemotherapy outcomes. Results The model demonstrated a prediction accuracy of 70–80% in forecasting chemotherapy responses in both test and validation cohorts. Notably, tumor-associated neutrophils emerged as significant predictors of treatment efficacy. Further single-cell analyses from cancer tissues revealed different neutrophil subgroups with potential antitumor activities suggesting their usefulness as biomarkers for treatment decisions. Conclusions This study confirms the utility of machine learning in advancing personalized medicine for gastric cancer by identifying tumor-associated neutrophils and their subgroups as key indicators of chemotherapy response. These findings could lead to more tailored and effective treatment plans for patients.

show abstract

Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer

Cited by 8 publications

References 91 publications

Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients

Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients

Advancements in medical diagnosis and treatment through machine learning: A review

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures

Contact Info

Product

Resources

About