68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours

Mapelli, Paola; Bezzi, Carolina; Palumbo, Diego; Canevari, Carla; Ghezzo, Samuele; Gajate, Ana Maria Samanes; Catalfamo, barbara; Messina, Antonio; Presotto, Luca; Guarnaccia, Alberto; Bettinardi, Valentino; Muffatti, Francesca; Andreasi, Valentina; Lena, Marco Schiavo; Partelli, Stefano; Falconi, Massimo; Scifo, Paola; Cobelli, Francesco De

doi:10.1007/s00259-022-05677-0

Cited by 27 publications

(13 citation statements)

References 36 publications

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“…Radiomics represents the process of extracting quantitative features to transform images into high-dimensional data for capturing deeper information to support decision-making [7][8][9][10][11]. Current studies have shown its potential for pancreatic precision medicine, especially in diagnosis and management of pancreatic tumors [12][13][14]. Although the main use of radiomics lies in oncology, the radiomics approach is suitable for non-oncological research based on its nature [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

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Section: Introductionmentioning

confidence: 99%

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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“…We had only one study applied PET-CT grading PNETs and found AUROC of 0.864 in the tumor grade prediction model which showed good forecasting ability ( 47 ). Thus, more investigation into PET-CT will be useful in developing AI models, which showing good predictive performance (AUC = 0.992) and can detect cell surface expression of somatostatin receptors ( 55 , 56 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Yan,

Chen,

Liu

et al. 2024

Front. Oncol.

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BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

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“…Two nuclear medicine doctors, JG and SX, who had each worked with 18 F-FDG and 68 Ga-DOTAT ATA E PET/MR imaging for more than three years, hand-drew 3D volumes of interest (VOIs). Then, the 18 F-FDG PET FSUVmax, MTV, and TLG parameters, as well as the 68 Ga-DOTATATE PET maximum standardised uptake value (GSUVmax), somatostatin receptor density (SRD), and total lesion somatostatin receptor density (TLSRD) parameters, were taken from these primary tumors [8]. MTV was measured based on tumor segmentation using a 40% FSUVmax threshold, while TLG was defined as FSUVmean x MTV.…”

Section: Quantitative Image Analysesmentioning

confidence: 99%

“…68 Ga-DOTANOC PET-based SUVmax values can serve as a reflection of SSTR2 density [7], whereas 18 F-FDG PET uptake values correspond to increases in metabolic activity at the cellular and tissue levels. Previous research efforts have demonstrated the complementary value of combining quantitative parameters derived from 68 Ga-DOTA-peptides and 18 F-FDG PET imaging analyses when evaluating PNET aggressiveness and conducting prognostic assessments [8,9]. In contrast, apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) from the MR sequence of PET/MR correlate with tumor cellularity such that they may offer value when evaluating tumor grading [10,11].…”

Section: Introductionmentioning

confidence: 99%

The potential application of MR-derived ADCmin values from 68Ga-DOTATATE and 18F-FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors

Gao

et al. 2023

EJNMMI Res

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Background To evaluate the utility of 68Ga-DOTATATE and 18F-FDG PET/MR for prediction of grade and stage of pancreatic neuroendocrine tumors (PNETs), and to examine the correlation between parameters obtained from FDG PET and diffusion-weighted imaging (DWI) MR parameters. Methods A retrospective study using 68Ga-DOTATATE and 18F-FDG PET/MR imaging was performed between April 2020 and May 2022 on 46 individuals with histologically confirmed PNETs. Metabolic tumor volume (MTV), maximum standardized uptake value (FSUVmax), and tumor lesion glycolysis (TLG) for FDG; somatostatin receptor density (SRD), maximum standardized uptake value (GSUVmax), and total lesion somatostatin receptor density (TLSRD) for DOTATATE; and minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values for MRI, respectively. We performed Spearman’s correlation analysis to examine the links between these variables and primary tumor stage and grading. Results Higher PNET grading was associated with higher FSUVmax, MTV, and TLG values (P < 0.05). TLG, SRD, ADCmin, and ADCmean values were correlated with N staging, while SRD, MTV, ADCmin, TLG, and ADCmean were associated with M staging. Notably, ADCmin was a negative correlation between FSUVmax (r = − 0.52; P < 0.001), MTV (r = − 0.50; P < 0.001), and TLG (r = − 0.56; P < 0.001). Conclusions This study highlights significant correlative relationships between FDG PET-derived parameters and ADCmin. ADCmin may offer utility as a tool for PNET staging and grading in lieu of FDG PET. 68Ga-DOTATATE PET/MR alone may be a sufficient alternative to dual tracer PET/MR when conducting grading and staging of primary PNETs.

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68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours

Cited by 27 publications

References 36 publications

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

The potential application of MR-derived ADCmin values from 68Ga-DOTATATE and 18F-FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors

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