A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer

Tokuda, Yukiko; Suzuki, Yuki; Oda, Soya; Sota, Yoshiaki; Naoi, Yasuto; Yanagawa, Masahiro; Motoyama, Yuichi; Kobayashi, Kosuke; Hata, Akinori; Yamagata, Kazuki; Doi, Shuhei; Nishigaki, Daiki; Tomiyama, Miyuki; Ueno, Midori; Fujita, Yuka; Shimazu, Kenzo; Morii, Eiichi; Kido, Shoji; Hori, Masatoshi; Tomiyama, Noriyuki

doi:https://doi.org/10.1016/j.ejrad.2026.112824

A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer

Tokuda, Yukiko; Suzuki, Yuki; Oda, Soya et al.

European Journal of Radiology, 2026, 200, 112824

Number of Access:13（2026-06-18 09:43 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/104870

Link to primary information

File	Format	Terms of use	Size	Views	Date.Available	Description	information
EurJRadiol_200_112824	pdf	None	199 KB		2027.04.07
EurJRadiol_200_112824_figs	pdf	None	597 KB		2027.04.07	Figure 1–4

Creative Commons : Attribution - NonCommercial - NoDerivatives

Item Information

Output File Export EndNote Basic Export Mendeley

Title	A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer
Creator	NRID 1000050522744 1000050522744 ORCID 0000-0001-7520-3310 0000-0001-7520-3310 researchmap permalink yukiko_tokuda yukiko_tokuda Scopus Author ID 8872388300 8872388300 CreatorName Tokuda, Yukiko
	NRID 1000020845599 1000020845599 ORCID 0000-0002-1572-5771 0000-0002-1572-5771 researchmap permalink ykszk ykszk Scopus Author ID 57204202740 57204202740 CreatorName Suzuki, Yuki
	CreatorName Oda, Soya
	NRID 1000080943779 1000080943779 ORCID 0000-0001-5963-652X 0000-0001-5963-652X researchmap permalink y_sota123 y_sota123 Scopus Author ID 56308090600 56308090600 CreatorName Sota, Yoshiaki
	NRID 1000030646211 1000030646211 CreatorName Naoi, Yasuto
	NRID 1000000546872 1000000546872 ORCID 0000-0002-0911-6769 0000-0002-0911-6769 researchmap permalink myMY3434 myMY3434 Scopus Author ID 36891136200 36891136200 CreatorName Yanagawa, Masahiro
	NRID 1000060948074 1000060948074 researchmap permalink y_motoyama y_motoyama Scopus Author ID 57208337832 57208337832 CreatorName Motoyama, Yuichi
	CreatorName Kobayashi, Kosuke
	NRID 1000010838600 1000010838600 ORCID 0000-0003-1149-7094 0000-0003-1149-7094 researchmap permalink hataakinori hataakinori Scopus Author ID 57188730692 57188730692 CreatorName Hata, Akinori
	CreatorName Yamagata, Kazuki
	CreatorName Doi, Shuhei
	ORCID 0000-0002-7006-3408 0000-0002-7006-3408 CreatorName Nishigaki, Daiki
	ORCID 0009-0008-4019-8222 0009-0008-4019-8222 Scopus Author ID 58959784300 58959784300 CreatorName Tomiyama, Miyuki
	ORCID 0009-0000-1506-1306 0009-0000-1506-1306 Scopus Author ID 57201355770 57201355770 CreatorName Ueno, Midori
	CreatorName Fujita, Yuka
	NRID 1000030448039 1000030448039 ORCID 0000-0002-0837-2357 0000-0002-0837-2357 researchmap permalink _-23f _-23f Scopus Author ID 7005224756 7005224756 CreatorName Shimazu, Kenzo
	NRID 1000010283772 1000010283772 ORCID 0000-0001-8490-3650 0000-0001-8490-3650 researchmap permalink read0046454 read0046454 Scopus Author ID 7003914326 7003914326 CreatorName Morii, Eiichi
	NRID 1000090314814 1000090314814 ORCID 0000-0001-5293-4110 0000-0001-5293-4110 researchmap permalink read0196068 read0196068 Scopus Author ID 7004389358 7004389358 CreatorName Kido, Shoji
	NRID 1000000346206 1000000346206 researchmap permalink read0092260 read0092260 CreatorName Hori, Masatoshi
	NRID 1000050294070 1000050294070 ORCID 0000-0001-9918-7327 0000-0001-9918-7327 researchmap permalink 3430 3430 Scopus Author ID 7006024942 7006024942 CreatorName Tomiyama, Noriyuki
Subject	Breast
	Breast cancer
	Magnetic resonance imaging
	Pathological complete response
	Radiogenomics
Description	Tokuda Y., Suzuki Y., Oda S., et al. A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer. European Journal of Radiology 200, 112824 (2026); https://doi.org/10.1016/j.ejrad.2026.112824.
Abstract	Purpose: Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is strongly associated with improved survival. Radiogenomic approaches integrating MRI. radiomics with gene signatures may enhance pretreatment prediction of therapeutic. response. This study aimed to develop a radiogenomic model for predicting pCR in breast cancer using MRI radiomics combined with an immune-related 23-gene signature (IRSN-23). Materials and Methods: We retrospectively analyzed 112 women with histologically confirmed breast cancer (pCR, n = 21; non-pCR, n = 91) who underwent pre-NAC MRI and had IRSN-23 scores derived from tumor tissue. Patients were assigned to training (n = 100) and validation (n = 12) cohorts. From pre-NAC MRI, 100 radiomic features were extracted. A support vector classifier was trained to predict pCR using three models: radiomics alone, IRSN-23 alone, and the combined radiogenomic model. Receiver operating characteristic (ROC) analysis and areas under the curve (AUC) were calculated. Kaplan–Meier survival analyses assessed relapse-free survival (RFS) and overall survival (OS) based on model-predicted pCR. Results: In the training cohort, AUCs (95 % confidence interval [CI]) were 0.83 (0.73–0.92) for IRSN-23, 0.76 (0.63–0.89) for MRI radiomics, and 0.93 (0.86–1.00) for the combined model. In the validation cohort, corresponding AUCs were 0.78 (0.49–1.00), 0.59 (0.20–0.99), and 0.89 (0.69–1.00). The combined model significantly outperformed MRI ( p = 0.007) and IRSN-23 ( p = 0.031) in the training cohort. Differences in RFS and OS according to model-predicted pCR did not reach statistical significance, although patients predicted to achieve pCR showed a trend toward longer survival. Conclusion: Integrating MRI radiomics with IRSN-23 improved pCR prediction compared with either modality alone and may support treatment planning, while the observed associations with survival outcomes should be interpreted as exploratory.
Publisher	Elsevier Ireland Ltd
Source Title	European Journal of Radiology
Volume (Issue)	200
Page	112824
Date of Issued	2026-04-07
Language	English
Handle URL	https://hdl.handle.net/11094/104870
PISSN	0720048X
eISSN	18727727
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1016/j.ejrad.2026.112824
Access Rights	embargoed access
CopyRight	© 2026. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
CopyRight	The full-text file will be made open to the public on 7 April 2027 in accordance with the publisher's policy.
oaire:version	Accepted Manuscript
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	European Journal of Radiology. 2026, 200, p. 112824
DC.title	A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer
DC.creator	Tokuda, Yukiko
	Suzuki, Yuki
	Oda, Soya
	Sota, Yoshiaki
	Naoi, Yasuto
	Yanagawa, Masahiro
	Motoyama, Yuichi
	Kobayashi, Kosuke
	Hata, Akinori
	Yamagata, Kazuki
	Doi, Shuhei
	Nishigaki, Daiki
	Tomiyama, Miyuki
	Ueno, Midori
	Fujita, Yuka
	Shimazu, Kenzo
	Morii, Eiichi
	Kido, Shoji
	Hori, Masatoshi
	Tomiyama, Noriyuki
DC.publisher	Elsevier Ireland Ltd
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2026-04-07
DC.identifier	https://doi.org/10.1016/j.ejrad.2026.112824
DC.subject	Breast
	Breast cancer
	Magnetic resonance imaging
	Pathological complete response
	Radiogenomics
DC.description	Tokuda Y., Suzuki Y., Oda S., et al. A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer. European Journal of Radiology 200, 112824 (2026); https://doi.org/10.1016/j.ejrad.2026.112824.
DCTERMS.abstract	Purpose: Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is strongly associated with improved survival. Radiogenomic approaches integrating MRI. radiomics with gene signatures may enhance pretreatment prediction of therapeutic. response. This study aimed to develop a radiogenomic model for predicting pCR in breast cancer using MRI radiomics combined with an immune-related 23-gene signature (IRSN-23). Materials and Methods: We retrospectively analyzed 112 women with histologically confirmed breast cancer (pCR, n = 21; non-pCR, n = 91) who underwent pre-NAC MRI and had IRSN-23 scores derived from tumor tissue. Patients were assigned to training (n = 100) and validation (n = 12) cohorts. From pre-NAC MRI, 100 radiomic features were extracted. A support vector classifier was trained to predict pCR using three models: radiomics alone, IRSN-23 alone, and the combined radiogenomic model. Receiver operating characteristic (ROC) analysis and areas under the curve (AUC) were calculated. Kaplan–Meier survival analyses assessed relapse-free survival (RFS) and overall survival (OS) based on model-predicted pCR. Results: In the training cohort, AUCs (95 % confidence interval [CI]) were 0.83 (0.73–0.92) for IRSN-23, 0.76 (0.63–0.89) for MRI radiomics, and 0.93 (0.86–1.00) for the combined model. In the validation cohort, corresponding AUCs were 0.78 (0.49–1.00), 0.59 (0.20–0.99), and 0.89 (0.69–1.00). The combined model significantly outperformed MRI ( p = 0.007) and IRSN-23 ( p = 0.031) in the training cohort. Differences in RFS and OS according to model-predicted pCR did not reach statistical significance, although patients predicted to achieve pCR showed a trend toward longer survival. Conclusion: Integrating MRI radiomics with IRSN-23 improved pCR prediction compared with either modality alone and may support treatment planning, while the observed associations with survival outcomes should be interpreted as exploratory.
DC.rights	© 2026. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
DC.rights	The full-text file will be made open to the public on 7 April 2027 in accordance with the publisher's policy.
DC.format	104870
citation_title	A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer
citation_author	Tokuda, Yukiko
	Suzuki, Yuki
	Oda, Soya
	Sota, Yoshiaki
	Naoi, Yasuto
	Yanagawa, Masahiro
	Motoyama, Yuichi
	Kobayashi, Kosuke
	Hata, Akinori
	Yamagata, Kazuki
	Doi, Shuhei
	Nishigaki, Daiki
	Tomiyama, Miyuki
	Ueno, Midori
	Fujita, Yuka
	Shimazu, Kenzo
	Morii, Eiichi
	Kido, Shoji
	Hori, Masatoshi
	Tomiyama, Noriyuki
citation_publisher	Elsevier Ireland Ltd
citation_language	English
citation_date	2026-04-07
citation_journal_title	European Journal of Radiology
citation_volume	200
citation_firstpage	112824
citation_doi	https://doi.org/10.1016/j.ejrad.2026.112824
citation_public_url	https://hdl.handle.net/11094/104870

A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer

Link to primary information

Item Information

Show details