Machine Learning Models of Breast Cancer Risk Prediction

Islam, Md. Mohaimenul; Poly, Tahmina Narin

doi:10.1101/723304

Cited by 10 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in breast cancer, machine learning models have been used to predict the likelihood of recurrence and to guide the selection of adjuvant therapy. These models take into account various factors such as tumor size, grade, hormone receptor status, and genomic markers to make their predictions [ 25 , 26 , 27 , 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

View full text Add to dashboard Cite

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model’s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

show abstract

Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

“…[6] use deep learning convolutional neural network to predict breast cancer based on Mammograph MIAS database. [7] consider a set of machine learning algorithms to predict breast cancer i.e. : Decision Tree, Random Forest, K-nearest Neighbours, Support Vector Machine, Logistic Regression, and Artificial Neural Network.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy c-Means Clusterization and ANN- MLP Prediction of Malign Breast Cancer in a Cohort of Patients

Massaro¹,

Costantiello²,

Magaletti³

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…Most studies using machine learning in the medical arena targeted breast cancer and were concerned with three clinical endpoints: (1) the prediction of cancer susceptibility, (2) the prediction of cancer recurrence, and (3) the prediction of cancer survivability [17]. Recently, machine learning algorithms, which used a set of clinical features including risk factors as input, were used to make predictions on breast cancer patients [18]. The algorithms showing the best accuracy in these studies were the artificial neural network and the K-nearest neighbor algorithms, with almost all reported studies concerning cancer prediction using an artificial neural network as their primary predictor, as it has the ability to learn and model nonlinear and complex relationships [17].…”

Section: Preventive Measures According To Cancer Research Ukmentioning

confidence: 99%

Statistical Meta-Analysis of Risk Factors for Endometrial Cancer and Development of a Risk Prediction Model Using an Artificial Neural Network Algorithm

Hutt

Mihaies

Karteris

et al. 2021

Cancers

View full text Add to dashboard Cite

Objectives: In this study we wished to determine the rank order of risk factors for endometrial cancer and calculate a pooled risk and percentage risk for each factor using a statistical meta-analysis approach. The next step was to design a neural network computer model to predict the overall increase or decreased risk of cancer for individual patients. This would help to determine whether this prediction could be used as a tool to decide if a patient should be considered for testing and to predict diagnosis, as well as to suggest prevention measures to patients. Design: A meta-analysis of existing data was carried out to calculate relative risk, followed by design and implementation of a risk prediction computational model based on a neural network algorithm. Setting: Meta-analysis data were collated from various settings from around the world. Primary data to test the model were collected from a hospital clinic setting. Participants: Data from 40 patients notes currently suspected of having endometrial cancer and undergoing investigations and treatment were collected to test the software with their cancer diagnosis not revealed to the software developers. Main outcome measures: The forest plots allowed an overall relative risk and percentage risk to be calculated from all the risk data gathered from the studies. A neural network computational model to determine percentage risk for individual patients was developed, implemented, and evaluated. Results: The results show that the greatest percentage increased risk was due to BMI being above 25, with the risk increasing as BMI increases. A BMI of 25 or over gave an increased risk of 2.01%, a BMI of 30 or over gave an increase of 5.24%, and a BMI of 40 or over led to an increase of 6.9%. PCOS was the second highest increased risk at 4.2%. Diabetes, which is incidentally also linked to an increased BMI, gave a significant increased risk along with null parity and noncontinuous HRT of 1.54%, 1.2%, and 0.56% respectively. Decreased risk due to contraception was greatest with IUD (intrauterine device) and IUPD (intrauterine progesterone device) at −1.34% compared to −0.9% with oral. Continuous HRT at −0.75% and parity at −0.9% also decreased the risk. Using open-source patient data to test our computational model to determine risk, our results showed that the model is 98.6% accurate with an algorithm sensitivity 75% on average. Conclusions: In this study, we successfully determined the rank order of risk factors for endometrial cancer and calculated a pooled risk and risk percentage for each factor using a statistical meta-analysis approach. Then, using a computer neural network model system, we were able to model the overall increase or decreased risk of cancer and predict the cancer diagnosis for particular patients to an accuracy of over 98%. The neural network model developed in this study was shown to be a potentially useful tool in determining the percentage risk and predicting the possibility of a given patient developing endometrial cancer. As such, it could be a useful tool for clinicians to use in conjunction with other biomarkers in determining which patients warrant further preventative interventions to avert progressing to endometrial cancer. This result would allow for a reduction in the number of unnecessary invasive tests on patients. The model may also be used to suggest interventions to decrease the risk for a particular patient. The sensitivity of the model limits it at this stage due to the small percentage of positive cases in the datasets; however, since this model utilizes a neural network machine learning algorithm, it can be further improved by providing the system with more and larger datasets to allow further refinement of the neural network.

show abstract

Machine Learning Models of Breast Cancer Risk Prediction

Cited by 10 publications

References 10 publications

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Fuzzy c-Means Clusterization and ANN- MLP Prediction of Malign Breast Cancer in a Cohort of Patients

Statistical Meta-Analysis of Risk Factors for Endometrial Cancer and Development of a Risk Prediction Model Using an Artificial Neural Network Algorithm

Contact Info

Product

Resources

About