CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Jana Schwarzerova; Libor Stefek; Jiri Simpach; Lubomir Pavliska; Bogdan Walek; Lukas Evin; Valentýna Provazník; Wolfram Weckwerth; Stefan Reguli

doi:10.1007/978-3-032-08452-1_18

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Jana Schwarzerova (Corresponding author)
, Libor Stefek
, Jiri Simpach
, Lubomir Pavliska
, Bogdan Walek
, Lukas Evin
, Valentýna Provazník
, Wolfram Weckwerth
, Stefan Reguli (Corresponding author)

Functional and Evolutionary Ecology

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Abstract

The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.

Original language	English
Title of host publication	Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings
Editors	Ignacio Rojas, Francisco Ortuño, Fernando Rojas Ruiz, Luis Javier Herrera, Juan José Escobar, Olga Valenzuela
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	219-229
Number of pages	11
ISBN (Print)	9783032084514
DOIs	https://doi.org/10.1007/978-3-032-08452-1_18
Publication status	Published - 2026
Event	12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025 - Canaria, Spain Duration: 16 Jul 2025 → 18 Jul 2025

Publication series

Series	Lecture Notes in Computer Science
Volume	16051 LNCS
ISSN	0302-9743

Conference

Conference	12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025
Country/Territory	Spain
City	Canaria
Period	16/07/25 → 18/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Austrian Fields of Science 2012

302094 Precision medicine
106007 Biostatistics
102033 Data mining

Keywords

Clinical data
Cyberknife
Data mining
Machine Learning

Access to Document

10.1007/978-3-032-08452-1_18

Cite this

Schwarzerova, J., Stefek, L., Simpach, J., Pavliska, L., Walek, B., Evin, L., Provazník, V., Weckwerth, W., & Reguli, S. (2026). CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements. In I. Rojas, F. Ortuño, F. Rojas Ruiz, L. J. Herrera, J. J. Escobar, & O. Valenzuela (Eds.), Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings (pp. 219-229). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-08452-1_18

Schwarzerova, Jana ; Stefek, Libor ; Simpach, Jiri et al. / CyberKnife and Data Mining : Exploring Opportunities for Clinical Advancements. Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings. editor / Ignacio Rojas ; Francisco Ortuño ; Fernando Rojas Ruiz ; Luis Javier Herrera ; Juan José Escobar ; Olga Valenzuela. Springer Science and Business Media Deutschland GmbH, 2026. pp. 219-229 (Lecture Notes in Computer Science, Vol. 16051 LNCS).

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abstract = "The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.",

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note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025 ; Conference date: 16-07-2025 Through 18-07-2025",

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doi = "10.1007/978-3-032-08452-1\_18",

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isbn = "9783032084514",

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Schwarzerova, J, Stefek, L, Simpach, J, Pavliska, L, Walek, B, Evin, L, Provazník, V, Weckwerth, W & Reguli, S 2026, CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements. in I Rojas, F Ortuño, F Rojas Ruiz, LJ Herrera, JJ Escobar & O Valenzuela (eds), Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings. Springer Science and Business Media Deutschland GmbH, Lecture Notes in Computer Science, vol. 16051 LNCS, pp. 219-229, 12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025, Canaria, Spain, 16/07/25. https://doi.org/10.1007/978-3-032-08452-1_18

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements. / Schwarzerova, Jana (Corresponding author); Stefek, Libor; Simpach, Jiri et al.
Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings. ed. / Ignacio Rojas; Francisco Ortuño; Fernando Rojas Ruiz; Luis Javier Herrera; Juan José Escobar; Olga Valenzuela. Springer Science and Business Media Deutschland GmbH, 2026. p. 219-229 (Lecture Notes in Computer Science, Vol. 16051 LNCS).

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

TY - GEN

T1 - CyberKnife and Data Mining

T2 - 12th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2025

AU - Schwarzerova, Jana

AU - Stefek, Libor

AU - Simpach, Jiri

AU - Pavliska, Lubomir

AU - Walek, Bogdan

AU - Evin, Lukas

AU - Provazník, Valentýna

AU - Weckwerth, Wolfram

AU - Reguli, Stefan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.

AB - The integration of data mining with precision medicine is transforming healthcare by uncovering novel clinical insights and enhancing treatment accuracy in patients undergoing CyberKnife therapy. This pilot study explores the potential of data mining to improve patient outcomes by identifying hidden patterns and relationships within clinical data. We apply various data mining techniques, including classification, regression, clustering, and association rule mining, to analyze patient records, diagnostic information, and treatment outcomes. Leveraging advanced algorithms, we aim to refine disease prediction, optimize treatment plans, and support personalized medicine. Preliminary results indicate promising applications in predicting treatment success, identifying risk factors, and streamlining clinical decision-making. This research contributes to bridging the gap between data mining analytics and precision healthcare, opening new possibilities for advancing radiotherapy practices.

KW - Clinical data

KW - Cyberknife

KW - Data mining

KW - Machine Learning

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DO - 10.1007/978-3-032-08452-1_18

M3 - Contribution to proceedings

AN - SCOPUS:105022884945

SN - 9783032084514

T3 - Lecture Notes in Computer Science

SP - 219

EP - 229

BT - Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings

A2 - Rojas, Ignacio

A2 - Ortuño, Francisco

A2 - Rojas Ruiz, Fernando

A2 - Herrera, Luis Javier

A2 - Escobar, Juan José

A2 - Valenzuela, Olga

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 16 July 2025 through 18 July 2025

ER -

Schwarzerova J, Stefek L, Simpach J, Pavliska L, Walek B, Evin L et al. CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements. In Rojas I, Ortuño F, Rojas Ruiz F, Herrera LJ, Escobar JJ, Valenzuela O, editors, Bioinformatics and Biomedical Engineering - 12th International Conference, IWBBIO 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 219-229. (Lecture Notes in Computer Science, Vol. 16051 LNCS). doi: 10.1007/978-3-032-08452-1_18

CyberKnife and Data Mining: Exploring Opportunities for Clinical Advancements

Abstract

Publication series

Conference

UN SDGs

Austrian Fields of Science 2012

Keywords

Access to Document

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