Combining positron emission tomography/computed tomography, radiomics, and sentinel lymph node mapping for nodal staging of endometrial cancer patients.


Journal

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
ISSN: 1525-1438
Titre abrégé: Int J Gynecol Cancer
Pays: England
ID NLM: 9111626

Informations de publication

Date de publication:
03 2020
Historique:
received: 09 10 2019
revised: 31 12 2019
accepted: 06 01 2020
pubmed: 23 2 2020
medline: 25 8 2020
entrez: 22 2 2020
Statut: ppublish

Résumé

To evaluate the combination of positron emission tomography/computed tomography (PET/CT) and sentinel lymph node (SLN) biopsy in women with apparent early-stage endometrial carcinoma. The correlation between radiomics features extracted from PET images of the primary tumor and the presence of nodal metastases was also analyzed. From November 2006 to March 2019, 167 patients with endometrial cancer were included. All women underwent PET/CT and surgical staging: 60/167 underwent systematic lymphadenectomy (Group 1) while, more recently, 107/167 underwent SLN biopsy (Group 2) with technetium-99m +blue dye or indocyanine green. Histology was used as standard reference. PET endometrial lesions were segmented (n=98); 167 radiomics features were computed inside tumor contours using standard Image Biomarker Standardization Initiative (IBSI) methods. Radiomics features associated with lymph node metastases were identified (Mann-Whitney test) in the training group (A); receiver operating characteristic (ROC) curves, area under the curve (AUC) values were computed and optimal cut-off (Youden index) were assessed in the test group (B). In Group 1, eight patients had nodal metastases (13%): seven correctly ridentified by PET/CT true-positive with one false-negative case. In Group 2, 27 patients (25%) had nodal metastases: 13 true-positive and 14 false-negative. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of PET/CT for pelvic nodal metastases were 87%, 94%, 93%, 70%, and 98% in Group 1 and 48%, 97%, 85%, 87%, and 85% in Group 2, respectively. On radiomics analysis a significant association was found between the presence of lymph node metastases and 64 features. Volume-density, a measurement of shape irregularity, was the most predictive feature (p=0001, AUC=0,77, cut-off 0.35). When testing cut-off in Group B to discriminate metastatic tumors, PET false-negative findings were reduced from 14 to 8 (-43%). PET/CT demonstrated high specificity in detecting nodal metastases. SLN and histologic ultrastaging increased false-negative PET/CT findings, reducing the sensitivity of the technique. PET radiomics features of the primary tumor seem promising for predicting the presence of nodal metastases not detected by visual analysis.

Identifiants

pubmed: 32079712
pii: ijgc-2019-000945
doi: 10.1136/ijgc-2019-000945
doi:

Substances chimiques

Radiopharmaceuticals 0
Fluorodeoxyglucose F18 0Z5B2CJX4D

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

378-382

Informations de copyright

© IGCS and ESGO 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

Cinzia Crivellaro (C)

Nuclear Medicine Department, ASST di Monza San Gerardo Hospital, Monza, Lombardia, Italy cinzia.crivellaro@unimib.it.

Claudio Landoni (C)

Nuclear Medicine Department, ASST di Monza San Gerardo Hospital, Monza, Lombardia, Italy.
School of Medicine and Surgery, University of Milan-Biocca, Monza, Lombardia, Italy.

Federica Elisei (F)

Nuclear Medicine Department, ASST di Monza San Gerardo Hospital, Monza, Lombardia, Italy.

Alessandro Buda (A)

Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Manuela Bonacina (M)

University of Milan-Bicocca, Milano, Lombardia, Italy.

Tommaso Grassi (T)

Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Lavinia Monaco (L)

University of Milan-Bicocca, Milano, Lombardia, Italy.

Daniela Giuliani (D)

Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Irene Gotuzzo (I)

University of Milan-Bicocca, Milano, Lombardia, Italy.

Sonia Magni (S)

University of Milan-Bicocca, Milano, Lombardia, Italy.

Giampaolo Di Martino (G)

Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Martina Delle Marchette (M)

University of Milan-Bicocca, Milano, Lombardia, Italy.

Luca Guerra (L)

Nuclear Medicine Department, ASST di Monza San Gerardo Hospital, Monza, Lombardia, Italy.
School of Medicine and Surgery, University of Milan-Biocca, Monza, Lombardia, Italy.

Fabio Landoni (F)

School of Medicine and Surgery, University of Milan-Biocca, Monza, Lombardia, Italy.
Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Robert Fruscio (R)

School of Medicine and Surgery, University of Milan-Biocca, Monza, Lombardia, Italy.
Gynaecologic Oncology Surgical Unit, Obstetrics and Gynaecology Department, ASST-Monza, San Gerardo Hospital, Milano, Lombardia, Italy.

Cristina Messa (C)

University of Milan-Bicocca, Milano, Lombardia, Italy.
Tecnomed Foundation, University of Milan-Bicocca, Milano, Lombardia, Italy.

Elisabetta De Bernardi (E)

School of Medicine and Surgery, University of Milan-Biocca, Monza, Lombardia, Italy.

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Classifications MeSH