Tissue heterogeneity contributes to suboptimal precision of WHO 2010 scoring criteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreascriteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreas

Liszka, Łukasz

doi:10.5114/pjp.2016.65864

Cited by 4 publications

(2 citation statements)

References 81 publications

(210 reference statements)

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“…Similar results have been recently reported by Leeds et al [11]. Intratumor cellular heterogeneity is a confounding factor that may be responsible for this dissonance between the pre-and postoperative analyses [6,12]. Furthermore, obtaining an adequate number of cells for reliable Ki-67 index determination is challenging using FNA.…”

Section: Introductionsupporting

confidence: 76%

Endoscopic ultrasound-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors: a retrospective analysis of 110 cases

Paiella¹,

Landoni²,

Rota³

et al. 2020

Endoscopy

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Background Data on the reliability of the Ki-67 index and grading calculations from endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) of pancreatic neuroendocrine tumors (PanNETs) are controversial. We aimed to assess the accuracy of these data compared with histology. Methods Cytological analysis from EUS-FNA in patients with suspected PanNETs (n = 110) were compared with resection samples at a single institution. A minimum of 2000 cells were considered to be adequate for grading. Correlation and agreement between cytology and histology in grading and Ki-67 values, respectively, were investigated. Secondary outcomes included the diagnostic performance of EUS-FNA. Results EUS-FNA samples were adequate for PanNET diagnosis and PanNET grading in 98/110 (89.1 %) and 77/110 (70.0 %) patients, respectively; thus, 77 samples were adequate for comparing cytology vs. histology. There were 67 (62.0 %), 40 (36.4 %), and 1 (0.9 %) patients with a final diagnosis of G1, G2, and G3 tumors, respectively. EUS-FNA grading was concordant with surgical pathology in 81.8 % of patients; under- and overgrading occurred in 15.6 % and 2.6 %, respectively. The overall level of agreement for grading was moderate (Cohen’s κ = 0.59, 95 % confidence interval [CI] 0.34 – 0.78). Spearman’s rho for Ki-67 in tumors ≤ 20 mm and > 20 mm was strong and moderate, respectively (rho = 0.68, 95 %CI 0.47 – 0.83; rho = 0.59, 95 %CI 0.35 – 0.75). The Bland – Altman plot showed that the Ki-67 values were comparable and reproducible between the two measurements. Conclusions Although they were not available for a significant number of patients, grading and Ki-67 values from cytology correlated with histology moderately to strongly.

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Section: Introductionsupporting

confidence: 76%

Endoscopic ultrasound-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors: a retrospective analysis of 110 cases

Paiella¹,

Landoni²,

Rota³

et al. 2020

Endoscopy

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“…5% vs. 2% to define G1 NET), to stratify outcome, remain controversial. Some retrospective studies analyzed varying cutoff values in order to improve the discrimination between G1 and G2 NET as a basis for optimized therapy [13][14][15]. Similarly, Genc et al [15] distinguished between low-and high G2 PNEN, but a precise cutoff value was not defined.…”

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confidence: 99%

Prognostic Assessment of Non‐functioning Neuroendocrine Pancreatic Neoplasms as a Basis for Risk‐Adapted Resection Strategies

et al. 2019

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Background In contrast to exocrine pancreatic carcinomas, prognosis and treatment of pancreatic neuroendocrine neoplasms (PNEN) are significantly different. The variable growth pattern and associated clinical situation of functioning and non-functioning PNEN demand an individualized surgical approach. However, due to the scarce evidence associated with the rare disease, guidelines lack detailed recommendations for indication and for the required extent of surgical resection. Methods In a retrospective single-center study from 1990 to 2018, 239 patients with PNEN were identified. Clinical data were collected in the MaDoc database of the University Medical Center Mainz. A total of 155 non-functional PNEN were selected for further analysis. Results According to the classification of NET by the WHO in 2017, 28.8% (n = 40) of the tumors were G1, 61.9% (n = 86) G2, and 9.4% (n = 13) G3. In 73 patients, hepatic metastases were present. Sixty patients had lymph node metastasis. An R0 resection was achieved in 98 cases, an R1 situation in 10 cases. Five times, a tumor debulking was carried out (R2) and 5 times the operation was aborted without any resection because of the advanced tumor stage. A relapse occurred in 29 patients. Different prognostic factors (grade, tumor size, age) were analyzed. Grade-dependent 10-year overall survival rates were 79.5% (grade 1) and 60.1% (grade 2), respectively. The survival rate of grade 3 patients was limited to 66.7% after 13 months. Conclusion In our study, patients with non-functioning PNEN had a longer overall survival after successful R0 resection. The risk analysis confirmed a Ki-67 cutoff value of 5%, which divided a high-and low-risk group. Patients with a PNEC G3 (Ki-67 index [ 50%) had a very poor prognosis.Neuroendocrine neoplasms (NEN) are rare tumors, with an incidence of 5 per 100,000 persons per year, although autopsy reports estimate their prevalence to be much higher [1-3]. Recent advances in medical technology have resulted in an increased incidence of pancreatic neuroendocrine neoplasms (PNEN) [4]. PNEN represent approximately 3% of pancreatic malignancies. About 50-55% of them are This paper was presented as an oral presentation at the IAES meeting/ 48th World Congress of Surgery August 11-15, 2019 in Krako ´w, Poland.

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A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors

Ye,

Fang,

Peng

et al. 2023

Eur Radiol

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Objectives To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner. Methods Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model. Results A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001). Conclusions An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability. Clinical relevance statement The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy. Key Points • A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.

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Tissue heterogeneity contributes to suboptimal precision of WHO 2010 scoring criteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreascriteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreas

Cited by 4 publications

References 81 publications

Endoscopic ultrasound-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors: a retrospective analysis of 110 cases

Endoscopic ultrasound-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors: a retrospective analysis of 110 cases

Prognostic Assessment of Non‐functioning Neuroendocrine Pancreatic Neoplasms as a Basis for Risk‐Adapted Resection Strategies

A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors

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