Susceptibility Perturbation MRI Maps Tumor Infiltration into Mesorectal Lymph Nodes.


Journal

Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R

Informations de publication

Date de publication:
01 05 2019
Historique:
received: 27 11 2018
revised: 13 02 2019
accepted: 15 03 2019
pubmed: 22 3 2019
medline: 8 2 2020
entrez: 22 3 2019
Statut: ppublish

Résumé

Noninvasive characterization of lymph node involvement in cancer is an enduring onerous challenge. In rectal cancer, pathologic lymph node status constitutes the most important determinant of local recurrence and overall survival, and patients with involved lymph nodes may benefit from preoperative chemo and/or radiotherapy. However, knowledge of lymph node status before surgery is currently hampered by limited imaging accuracy. Here, we introduce Susceptibility-Perturbation MRI (SPI) as a novel source of contrast to map malignant infiltration into mesorectal lymph nodes. SPI involves multigradient echo (MGE) signal decays presenting a nonmonoexponential nature, which we show is sensitive to the underlying microstructure via susceptibility perturbations. Using numerical simulations, we predicted that the large cell morphology and the high cellularity of tumor within affected mesorectal lymph nodes would induce signature SPI decays. We validated this prediction in mesorectal lymph nodes excised from total mesorectal excision specimens of patients with rectal cancer using ultrahigh field (16.4 T) MRI. SPI signals distinguished benign from malignant nodal tissue, both qualitatively and quantitatively, and our histologic analyses confirmed cellularity and cell size were the likely underlying sources for the differences observed. SPI was then adapted to a clinical 1.5 T scanner, added to patients' staging protocol, and compared with conventional assessment by two expert radiologists. Nonmonoexponential decays, similar to those observed in the

Identifiants

pubmed: 30894376
pii: 0008-5472.CAN-18-3682
doi: 10.1158/0008-5472.CAN-18-3682
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2435-2444

Informations de copyright

©2019 American Association for Cancer Research.

Auteurs

Inês Santiago (I)

Neuroplasticity and Neural Activity Lab, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Radiology Department, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Nova Medical School, Campo Mártires da Pátria, Lisbon, Portugal.

João Santinha (J)

Computational Clinical Imaging Group, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Andrada Ianus (A)

Neuroplasticity and Neural Activity Lab, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Centre for Medical Imaging Computing, Department of Computer Science, University College London, London, United Kingdom.

Antonio Galzerano (A)

Pathology Department, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Rita Theias (R)

Pathology Department, Hospital Fernando Fonseca, Amadora, Portugal.

Joana Maia (J)

Systems Oncology Lab, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Maria J Barata (MJ)

Radiology Department, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Nuno Loução (N)

Philips Healthcare Iberia, Madrid, Spain.

Bruno Costa-Silva (B)

Systems Oncology Lab, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Antonio Beltran (A)

Pathology Department, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Celso Matos (C)

Radiology Department, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Noam Shemesh (N)

Neuroplasticity and Neural Activity Lab, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal. noam.shemesh@neuro.fchampalimaud.org.

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