Mr. Zan Klanecek Profile

Zan Klanecek

at Univ of Ljubljana

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Publications (6)

Proceedings Article | 29 May 2024 Paper

Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model

Y. Wang, Z. Klanecek, T. Wagner, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Proceedings Volume 13174, 131740P (2024) https://doi.org/10.1117/12.3027024

KEYWORDS: Mammography, Breast, Breast cancer, Tumor growth modeling, Feature extraction, Cancer detection, Computer aided detection, Risk assessment, Nipple, Artificial intelligence

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Proceedings Article | 29 May 2024 Paper

Incorporating longitudinal screening data into image-based breast cancer risk assessment

T. Wagner, Z. Klanecek, Y. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Proceedings Volume 13174, 131740T (2024) https://doi.org/10.1117/12.3026995

KEYWORDS: Data modeling, Breast cancer, Education and training, Risk assessment, Performance modeling, Cancer, Cancer detection, Mammography, Solid modeling

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Proceedings Article | 29 May 2024 Paper

Longitudinal interpretability of deep learning-based breast cancer risk prediction model: comparison of different attribution methods

Zan Klanecek, Yao-Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Brayden Schott, Ali Deatsch, Andrej Studen, Kristijana Hertl, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Hilde Bosmans, Robert Jeraj

Proceedings Volume 13174, 131741W (2024) https://doi.org/10.1117/12.3025858

KEYWORDS: Cancer, Breast, Breast cancer, Mammography, Cancer detection, Deep learning, Medical physics, Visualization, Risk assessment, Digital mammography

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Proceedings Article | 3 April 2024 Poster + Paper

Transfer learning from breast cancer detection models for image-based breast cancer risk prediction

T. Wagner, Z. Klanecek, Y. Wang, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Proceedings Volume 12927, 1292725 (2024) https://doi.org/10.1117/12.3006670

KEYWORDS: Data modeling, Tumor growth modeling, Breast cancer, Solid modeling, Education and training, Cancer detection, Performance modeling, Cancer, Risk assessment, Computer aided detection

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Proceedings Article | 13 July 2022 Paper

Applying radiomics-based risk prediction models from digital mammography to digital breast tomosynthesis: a preliminary reliability survey

Y.-K. Wang, Ž. Klaneček, T. Wagner, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans

Proceedings Volume 12286, 1228614 (2022) https://doi.org/10.1117/12.2624599

KEYWORDS: Reliability, Digital breast tomosynthesis, Breast, Feature extraction, Feature selection, Digital mammography

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Aim: This project is part of a long-term goal to apply radiomics-based risk prediction models designed for twodimensional (2D) digital mammography (DM) to three-dimensional (3D) digital breast tomosynthesis (DBT), using either the DBT projection views (PV) or the reconstructed planes. In this work, 2 fundamental aspects related to PVs were explored: (1) finding robust radiomic features for both DM and PV, and (2) selecting robust and informative radiomic features for both 2D and 3D modalities by requiring respectively invariance and noninvariance of these features across DBT projections. Methods: DM and PVs from combined DM and DBT acquisitions of phantom and patients were used in this study. Robust radiomic features in these images were identified by the intra-class correlation coefficient (ICC) between DM and the central PV for DBT. Then, projection invariant and noninvariant radiomic features of PVs for different projection angles were also characterized by ICC. Finally, selected projection invariant features of PVs were applied on a DM breast density classifier and their predictive power was compared to the results of DM. Results: A total of 70 out of 93 extracted radiomic features (75%) showed at least moderate reliability (ICC>0.5) between DM and the central PV. In addition, a decrease of feature reliability along increasing angular range was observed on both real and simulated datasets. With projection angle invariance as the feature selection method, overfitting of a DM density classifier was reduced. Conclusions: A large portion of radiomic features was robust between DM and the central PV without specific harmonization, suggesting that some parts of the radiomic features of DM can be applied to the DBT projection dataset. Additionally, 3D DBT could also benefit 2D DM through the projection angle variation test. Projectioninvariant features with better robustness could be selected for 2D DM which was preliminary validated by a density classification task, while projection non-invariant features which incorporate 3D information in the PVs may be suitable for 3D DBT.

Proceedings Article | 4 April 2022 Poster + Paper

A study on the impact of parameter settings on the biological reproducibility and sensitivity of extracted radiomic features from full field digital mammography images

Z. Klanecek, T. Wagner, Y. K. Wang, L. Cockmartin, K. Hertl, K. Jarm, M. Krajc, N. W. Marshall, A. Studen, M. Vrhovec, H. Bosmans, R. Jeraj

Proceedings Volume 12031, 120311S (2022) https://doi.org/10.1117/12.2611056

KEYWORDS: Breast, Feature extraction, Breast cancer, Biological research, Mammography, Digital mammography, Reliability, X-ray detectors, Oncology

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