Intelligent Lesion Selection: A Novel Method for Longitudinal Assessment of Breast Cancer Lung Metastases
Keywords: Longitudinal disease assessment, Lung metastasis in breast cancer, Computed Tomography (CT), Deep learning, Lesion detection and selection
TL;DR: A novel pipeline with temporal lesion pair classification improves tumor size progression assessment by 42% and treatment response classification accuracy by 22% in CT-based metastatic lung lesion monitoring.
Abstract: Breast cancer, the second most common cancer globally, often metastasizes to the lungs, requiring frequent computed tomography (CT) scans to monitor disease progression. Manual analysis by radiologists is time-consuming and prone to variability, underscoring the need for automated systems to enhance accuracy and efficiency. The goal of such systems is to optimize processes like RECIST score calculation for tumor response assessment. This study presents a pipeline for the automated temporal analysis of breast cancer lung metastases. Existing lung nodule detection and segmentation models were adapted for detecting and segmenting breast cancer metastases. Registration-based lesion tracking was incorporated, and a novel Temporal Lesion Pair Classifier was developed to identify significant lesions and estimate tumor load evolution by summing their diameters, following an adaptation of the RECIST guidelines. Evaluated on a unique dataset of breast cancer patients, each with multiple annotated CT scans at different disease stages, the proposed pipeline demonstrated a 42% reduction in median tumor size progression discrepancy for consecutive study pairs and improved tumor response classification accuracy by 22% at the patient level.
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Application: Radiology
Paper Type: Both
Registration Requirement: Yes
Visa & Travel: Yes
Submission Number: 193
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