Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification

Daniele Perlo; Luca Berton; Alessia Delpiano; Francesca Menchini; Stefano Tibaldi; Marco Grosso; Paolo Fonio

doi:10.1109/BigData55660.2022.10020613

Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification

Daniele Perlo^*, Luca Berton, Alessia Delpiano, Francesca Menchini, Stefano Tibaldi, Marco Grosso, Paolo Fonio

^*Corresponding author for this work

Translational Radiomics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4425-4433
Number of pages	9
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020613
Publication status	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Classification
Colorectal Cancer
Computed Tomography
Deep Learning
Genomics Mutation
Liver Carcinoma
Self-Supervised Learning

Access to Document

10.1109/BigData55660.2022.10020613

Cite this

Perlo, D., Berton, L., Delpiano, A., Menchini, F., Tibaldi, S., Grosso, M., & Fonio, P. (2022). Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 4425-4433). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020613

Perlo, Daniele ; Berton, Luca ; Delpiano, Alessia et al. / Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 4425-4433 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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title = "Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification",

abstract = "The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.",

keywords = "Classification, Colorectal Cancer, Computed Tomography, Deep Learning, Genomics Mutation, Liver Carcinoma, Self-Supervised Learning",

author = "Daniele Perlo and Luca Berton and Alessia Delpiano and Francesca Menchini and Stefano Tibaldi and Marco Grosso and Paolo Fonio",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020613",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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Perlo, D, Berton, L, Delpiano, A, Menchini, F, Tibaldi, S, Grosso, M & Fonio, P 2022, Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 4425-4433, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020613

Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification. / Perlo, Daniele; Berton, Luca; Delpiano, Alessia et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 4425-4433 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification

AU - Perlo, Daniele

AU - Berton, Luca

AU - Delpiano, Alessia

AU - Menchini, Francesca

AU - Tibaldi, Stefano

AU - Grosso, Marco

AU - Fonio, Paolo

PY - 2022

Y1 - 2022

N2 - The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.

AB - The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.

KW - Classification

KW - Colorectal Cancer

KW - Computed Tomography

KW - Deep Learning

KW - Genomics Mutation

KW - Liver Carcinoma

KW - Self-Supervised Learning

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U2 - 10.1109/BigData55660.2022.10020613

DO - 10.1109/BigData55660.2022.10020613

M3 - Conference contribution

AN - SCOPUS:85147981892

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 4425

EP - 4433

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

Y2 - 17 December 2022 through 20 December 2022

ER -

Perlo D, Berton L, Delpiano A, Menchini F, Tibaldi S, Grosso M et al. Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 4425-4433. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020613

Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification

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