scholarly journals A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 870
Author(s):  
Alessandro Bevilacqua ◽  
Diletta Calabrò ◽  
Silvia Malavasi ◽  
Claudio Ricci ◽  
Riccardo Casadei ◽  
...  
Keyword(s):  
Neuroendocrine Tumour ◽  
Area Under The Curve ◽  
Tumour Volume ◽  
Assessment Method ◽  
Second Order ◽  
Tumour Grade ◽  
Non Invasive ◽  
Pet Ct ◽  
Median Test ◽  
Fdg Pet Ct

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

scholarly journals Potential Utility of FDG PET-CT as a Non-invasive Tool for Monitoring Local Immune Responses

2017 ◽  
Vol 17 (4) ◽  
pp. 384 ◽  
Author(s):  
Seungho Lee ◽  
Seohee Choi ◽  
Sang Yong Kim ◽  
Mi Jin Yun ◽  
Hyoung-Il Kim
Keyword(s):  
Immune Responses ◽  
Fdg Pet ◽  
Potential Utility ◽  
Non Invasive ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Non‑invasive monitoring of cisplatin and erlotinib efficacy against lung cancer in orthotopic SCID mouse models by small animal FDG‑PET/CT and CT

2018 ◽  
Author(s):  
Tamaki Otani ◽  
Kazuya Kondo ◽  
Hiromitsu Takizawa ◽  
Koichiro Kajiura ◽  
Haruhiko Fujino ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mouse Models ◽  
Fdg Pet ◽  
Scid Mouse ◽  
Small Animal ◽  
Invasive Monitoring ◽  
Non Invasive ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Metabolic tumour volume on 18F-FDG PET/CT predicts extended pathological T stages in patients with renal cell carcinoma at staging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dongwoo Kim ◽  
Narae Lee ◽  
Suk Hyun Lee ◽  
Hyun Jeong Kim ◽  
Hye-Suk Hong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Fdg Pet ◽  
Renal Cell ◽  
Univariate Analysis ◽  
Tumour Volume ◽  
Metabolic Tumour Volume ◽  
Pathologic Features ◽  
Pet Ct ◽  
Fdg Pet Ct

AbstractWe evaluated the predictive value of 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/CT (PET/CT) for extended pathological T (pT) stages (≥ pT3a) in Renal cell carcinoma (RCC) patients at staging. Thirty-eight RCC patients who underwent 18F-FDG PET/CT at staging, followed by radical nephrectomy between September 2016 and September 2018, were included in this prospective study. Patients were classified into two groups (limited pT stage: stage T1/2, n = 17; extended pT stage: T3/4, n = 21). Univariate and multivariate logistic regression analyses were performed to identify clinicopathological and metabolic variables to predict extended pT stages. 18F-FDG metabolic parameters were compared in relation to International Society of Urological Pathology (ISUP) grade and lymphovascular invasion (LVI). In univariate analysis, maximum standardised uptake value, metabolic tumour volume (MTV), and ISUP grade were significant. In multivariate analysis, MTV was the only significant factor of extended pT stages. With a cut-off MTV of 21.2, an area under the curve was 0.944, which was higher than 0.824 for clinical T stages (p = 0.037). In addition, high MTV, but not tumour size, was significantly correlated with aggressive pathologic features (ISUP grade and LVI). High glycolytic tumour volume on 18F-FDG PET/CT in RCC patients at staging is predictive of extended pT stages which could aid decision-making regarding the best type of surgery.

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