Harnessing Fusion Modeling for Enhanced Breast Cancer Classification through Interpretable Artificial Intelligence and In-Depth Explanations

An evaluation of the effectiveness of machine learning prediction models in assessing breast cancer risk

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p &lt; 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging. 

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study

This Special Issue is dedicated to discussing which are the advantages, challenges and open issues in the application of the agent-based approach as a part of the digital transformation in the healthcare sector. Agent-based technology in healthcare optimises resource allocation and coordination and supports clinical decision-making. Challenges, such as model reliability and interdisciplinary collaboration, must be addressed for widespread adoption. Embracing this technology promises improved healthcare delivery and better patient outcomes. Six papers, out of the many submitted, have been accepted for publication, each one discussing an aspect of this broad field. 

Agent-based systems in healthcare.

Exploring the Feasibility and Implications of Cranioencephalic Computed Tomography in HER2-positive Breast Cancer: A Pilot Study

Simple Summary Breast cancer is a heterogeneous disease characterized by different risks of relapse, which makes it challenging to predict progression and select the most appropriate follow-up strategies. With the ever-growing adoption of Electronic Health Records, there are great opportunities to leverage the amount of data collected routinely in electronic format for secondary purposes. Machine Learning algorithms offer the ability to analyze large amounts of data and reveal insights that might otherwise go undetected. In this study, we have applied several algorithms to predict 5-year breast cancer recurrence from health data. We compared whether taking advantage of both structured and unstructured data from health records yields better prediction results than using any of the sources separately. These algorithms are valuable tools to help clinicians effectively integrate large amounts of data into their decision-making and are key to improving risk stratification and providing personalized assistance to patients. Abstract Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.

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Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Breast cancer survival analysis agents for clinical decision support

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Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Breast cancer survival analysis agents for clinical decision support