MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).


Journal

La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625

Informations de publication

Date de publication:
Dec 2020
Historique:
received: 10 02 2020
accepted: 27 04 2020
pubmed: 16 5 2020
medline: 20 11 2020
entrez: 16 5 2020
Statut: ppublish

Résumé

To determine whether MRI T2-weighted sequences-based texture analysis (TA) can predict histopathological tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemo-radiotherapy (nCRT). Data on patients undergoing curative-intent surgery for LARC were collected. Patients with a complete pathological response, or TRG1 according to Mandard's system were classified as responders, while patients with TRG ≥ 2 were classified as non-responders. Tumor TA was performed on each patient's paraxial T2w MRI in both pre- and post-nCRT scans, in order to extract histograms, gray-level co-occurrence matrix (GLCM) and run-length matrix (RLM) texture parameters. For features that showed a significant difference between the two groups, a receiver operating characteristic (ROC) curve was drawn. Overall, 62 patients with LARC, treated with nCRT and resective surgery at our institution between 2013 and 2019 were identified. Only post-nCRT GLCM maximum probability showed a significant difference between the two groups (2909 ± 4479 in responders vs. 6515 ± 8990 in non- responders; p = 0.039); at the ROC curve, Youden index showed a sensitivity of 14% and a specificity of 100% for this parameter. MRI T2-weighted sequences-based TA was not effective in predicting pathological complete response to nCRT in patients with LARC. Further studies are needed to thoroughly investigate the potential of MRI TA in this setting.

Identifiants

pubmed: 32410063
doi: 10.1007/s11547-020-01215-w
pii: 10.1007/s11547-020-01215-w
doi:

Substances chimiques

Antimetabolites, Antineoplastic 0
Capecitabine 6804DJ8Z9U
Fluorouracil U3P01618RT

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1216-1224

Auteurs

Filippo Crimì (F)

Department of Medicine-DIMED, Institute of Radiology, University Hospital of Padova, Padua, Italy.

Giulia Capelli (G)

Clinica Chirurgica I, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University Hospital of Padova, Via Nicolò Giustiniani 2, 35128, Padua, Italy.

Gaya Spolverato (G)

Clinica Chirurgica I, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University Hospital of Padova, Via Nicolò Giustiniani 2, 35128, Padua, Italy. gaya.spolverato@unipd.it.

Quoc Riccardo Bao (QR)

Clinica Chirurgica I, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University Hospital of Padova, Via Nicolò Giustiniani 2, 35128, Padua, Italy.

Anna Florio (A)

Department of Medicine-DIMED, Institute of Radiology, University Hospital of Padova, Padua, Italy.

Sebastiano Milite Rossi (S)

Department of Medicine-DIMED, Institute of Radiology, University Hospital of Padova, Padua, Italy.

Diego Cecchin (D)

Nuclear Medicine Unit, Department of Medicine-DIMED, University Hospital of Padova, Padua, Italy.

Laura Albertoni (L)

Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University Hospital of Padova, Padua, Italy.

Cristina Campi (C)

Department of Mathematics "Tullio Levi-Civita", University of Padova, Padua, Italy.

Salvatore Pucciarelli (S)

Clinica Chirurgica I, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University Hospital of Padova, Via Nicolò Giustiniani 2, 35128, Padua, Italy.

Roberto Stramare (R)

Department of Medicine-DIMED, Institute of Radiology, University Hospital of Padova, Padua, Italy.

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Classifications MeSH