Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours

Purpose Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [ 68 Ga]Ga-DOTATOC PET radiomics mig...

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Published in:European journal of nuclear medicine and molecular imaging 2024-07, Vol.51 (9), p.2774-2783
Main Authors: Mapelli, Paola, Bezzi, Carolina, Muffatti, Francesca, Ghezzo, Samuele, Canevari, Carla, Magnani, Patrizia, Schiavo Lena, Marco, Battistella, Anna, Scifo, Paola, Andreasi, Valentina, Partelli, Stefano, Chiti, Arturo, Falconi, Massimo, Picchio, Maria
Format: Article
Language:English
Subjects:
Cardiology
Diagnostic systems
Histology
Imaging
Imaging techniques
Lymph nodes
Machine learning
Medicine
Medicine & Public Health
Metastases
Metastasis
Neuroendocrine tumors
Nuclear Medicine
Oncology
Original Article
Orthopedics
Pancreas
Pancreatic cancer
Positron emission
Positron emission tomography
Radiology
Radiomics
Receptor density
Sensitivity
Somatostatin
Tumors
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Summary:Purpose Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [ 68 Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. Methods Seventy-two patients who performed preoperative [ 68 Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [ 68 Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models’ bACC, SN, SP, PPV and NPV were collected and compared (Kruskal–Wallis, Mann–Whitney). Results LN metastases were detected in 29/72 patients at histology. [ 68 Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p  
ISSN:1619-7070
1619-7089
DOI:10.1007/s00259-024-06730-w