Intelligent Lesion Selection: A Novel Method for Longitudinal Assessment of Breast Cancer Lung Metastases

Melika Qahqaie, Veronika A Zimmer, Eduardo Castaneda, Katariina Peltonen, Joonas Laaksolilj, Juho Lähteenmaa, Tobias Heimann, Andreas Maier, Dominik Neumann
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1342-1355, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-qahqaie26a, title = {Intelligent Lesion Selection: A Novel Method for Longitudinal Assessment of Breast Cancer Lung Metastases}, author = {Qahqaie, Melika and Zimmer, Veronika A and Castaneda, Eduardo and Peltonen, Katariina and Laaksolilj, Joonas and L\"ahteenmaa, Juho and Heimann, Tobias and Maier, Andreas and Neumann, Dominik}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1342--1355}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/qahqaie26a/qahqaie26a.pdf}, url = {https://proceedings.mlr.press/v301/qahqaie26a.html}, 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.} }
Endnote
%0 Conference Paper %T Intelligent Lesion Selection: A Novel Method for Longitudinal Assessment of Breast Cancer Lung Metastases %A Melika Qahqaie %A Veronika A Zimmer %A Eduardo Castaneda %A Katariina Peltonen %A Joonas Laaksolilj %A Juho Lähteenmaa %A Tobias Heimann %A Andreas Maier %A Dominik Neumann %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-qahqaie26a %I PMLR %P 1342--1355 %U https://proceedings.mlr.press/v301/qahqaie26a.html %V 301 %X 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.
APA
Qahqaie, M., Zimmer, V.A., Castaneda, E., Peltonen, K., Laaksolilj, J., Lähteenmaa, J., Heimann, T., Maier, A. & Neumann, D.. (2026). Intelligent Lesion Selection: A Novel Method for Longitudinal Assessment of Breast Cancer Lung Metastases. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1342-1355 Available from https://proceedings.mlr.press/v301/qahqaie26a.html.

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