Preoperative assessment of microvessel density in nonfunctioning pancreatic neuroendocrine tumors (NF-PanNETs)

A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumors

Objectives

This research aimed to retrospectively construct and authenticate ultrasomics models using endoscopic ultrasonography (EUS) images for forecasting the pathological grading of pancreatic neuroendocrine tumors (PNETs).

Methods

After confirmation through pathological examination, a retrospective analysis of 79 patients was conducted, including 49 with grade 1 PNETs and 30 with grade 2/3 PNETs. These patients were randomized to the training or test cohort in a 6:4 proportion. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimensionality of ultrasomics features derived from standard EUS images. These nonzero coefficient features were retained and applied to construct prediction models via eight machine-learning algorithms. The optimum ulstrasomics model was determined, followed by creating and evaluating a nomogram.

Results

Ultrasomics features of 107 were extracted, and only those with coefficients greater than zero were retained. The XGboost ultrasomics model performed exceptionally well, achieving AUCs of 0.987 and 0.781 in the training and test cohorts, respectively. Furthermore, an effective nomogram was developed and visually represented. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curve (CIC) displayed in the ulstrasomics model and nomogram demonstrated high accuracy. They provided significant net benefits for clinical decision-making.

Conclusions

A novel ulstrasomics model and nomogram were created and certified to predict the pathological grading of PNETs using EUS images. This study has the potential to provide valuable insights that improve the clinical applicability and efficacy of EUS in predicting the grading of PNETs.

Critical appraisal of the adequacy of surgical indications for non-functioning pancreatic neuroendocrine tumours

BACKGROUND

The lack of preoperative prognostic factors to accurately predict tumour aggressiveness in non-functioning pancreatic neuroendocrine tumours may result in inappropriate management decisions. This study aimed to critically evaluate the adequacy of surgical treatment in patients with resectable non-functioning pancreatic neuroendocrine tumours and investigate preoperative features of surgical appropriateness.

METHODS

A retrospective study was conducted on patients who underwent curative surgery for non-functioning pancreatic neuroendocrine tumours at San Raffaele Hospital (2002-2022). The appropriateness of surgical treatment was categorized as appropriate, potential overtreatment and potential undertreatment based on histologic features of aggressiveness and disease relapse within 1 year from surgery (early relapse).

RESULTS

A total of 384 patients were included. Among them, 230 (60%) received appropriate surgical treatment, whereas the remaining 154 (40%) underwent potentially inadequate treatment: 129 (34%) experienced potential overtreatment and 25 (6%) received potential undertreatment. The appropriateness of surgical treatment was significantly associated with radiological tumour size (P < 0.001), tumour site (P = 0.012), surgical technique (P < 0.001) and year of surgical resection (P < 0.001). Surgery performed before 2015 (OR 2.580, 95% c.i. 1.570 to 4.242; P < 0.001), radiological tumour diameter < 25.5 mm (OR 6.566, 95% c.i. 4.010 to 10.751; P < 0.001) and pancreatic body/tail localization (OR 1.908, 95% c.i. 1.119 to 3.253; P = 0.018) were identified as independent predictors of potential overtreatment. Radiological tumour size was the only independent determinant of potential undertreatment (OR 0.291, 95% c.i. 0.107 to 0.791; P = 0.016). Patients subjected to potential undertreatment exhibited significantly poorer disease-free survival (P < 0.001), overall survival (P < 0.001) and disease-specific survival (P < 0.001).

CONCLUSIONS

Potential overtreatment occurs in nearly one-third of patients undergoing surgery for non-functioning pancreatic neuroendocrine tumours. Tumour diameter emerges as the sole variable capable of predicting the risk of both potential surgical overtreatment and undertreatment.

Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms

INTRODUCTION

Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures.

AREAS COVERED

This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023.

EXPERT OPINION

A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.

Tumor vascularity on contrast-enhanced computed tomography as a predictive marker of metastatic potential for small nonfunctioning pancreatic neuroendocrine tumors

BACKGROUND

Although surgical resection is generally suggested for nonfunctioning pancreatic neuroendocrine tumors, observation can be proposed for carefully selected patients with small tumors. However, the indications for observation remain unclear.

METHODS

This retrospective study included 77 patients with nonfunctioning pancreatic neuroendocrine tumors, including small tumors (≤2.0 cm, n = 41), who received pancreatectomy. The ratio of the mean computed tomography value of a tumor in the late arterial/equilibrium phase (computed tomography a/e ratio) was used to evaluate tumor vascularity. Pathologic examinations of small tumors were conducted. The associations among the computed tomography a/e ratio, pathologic findings, and survival outcomes were investigated.

RESULTS

Small tumors were pathologically categorized by the degree of fibrosis as follows: medullary (n = 20), intermediate (n = 11), and fibrotic (n = 10). The fibrotic type had significantly lower computed tomography a/e ratios than the medullary type (median, 1.42 vs 2.03, P < .001). The median number of vessels with microscopic venous invasion was significantly higher in the fibrotic type than in the medullary type (4.5 vs 0.0, P < .001). The cutoff value of the computed tomography a/e ratio for predicting microscopic venous invasion was determined to be 1.54 by the receiver operating characteristic curve (area under the curve, 0.832; sensitivity, 80.0%; specificity, 83.9%; accuracy, 82.9%). Microscopic venous invasion was an independent prognostic factor for relapse-free survival in overall patients (hazard ratio 5.18, P = .017).

CONCLUSION

The computed tomography a/e ratio may be a useful predictor of the metastatic potential of nonfunctioning pancreatic neuroendocrine tumors and may help decide the indications of observation for small nonfunctioning pancreatic neuroendocrine tumors.

Usefulness of intratumoral perfusion analysis for assessing biological features of non-functional pancreatic neuroendocrine neoplasm

PURPOSE

Here, we evaluated the usefulness of intratumoral perfusion analysis using preoperative contrast-enhanced CT (E-CT) to assess biological features of non-functional pancreatic neuroendocrine neoplasms (NF-PanNENs).

METHODS

We retrospectively studied 44 patients who underwent curative surgery for NF-PanNENs. We used preoperative E-CT with compartment model analysis to calculate the tumor perfusion parameters K1 (inflow rate constant), 1/k2 (mean transit time), and K1/k2 (distribution volume). We assessed the association between perfusion parameters and biological features of NF-PanNENs, including the WHO classification tumor histopathological grade and prognosis after surgery.

RESULTS

Patients in this study had a neuroendocrine tumor (NET) G1 (n = 32) or NET G2 (n = 12). Neither NET G3 or NEC tumors were observed. Among perfusion parameters, K1 was the most accurate predictor of the high-grade tumor (AUC: 0.726). K1-low (< 0.028 s-1) was significantly associated with large tumors (≥ 20 mm) (p = 0.022), high mitotic index (p = 0.017), high Ki-67 index (p = 0.004), and lymphatic invasion (p = 0.025). Synchronous extra-pancreatic metastasis, including lymph node metastasis or liver metastasis, more frequently developed in K1-low patients than in K1-high patients (29% vs 4%, p = 0.025). Disease-free survival of patients with a K1-low tumor was poorer than that of patients with a K1-high tumor (p = 0.005). Furthermore, no patient with a K1-high tumor developed recurrence after initial surgery.

CONCLUSION

The perfusion parameters obtained using E-CT were significantly associated with biological features and prognosis of NF-PanNENs.

Pilot Study: FSHR Expression in Neuroendocrine Tumors of the Appendix

Appendix neuroendocrine neoplasm (ANEN) treatment is based on tumor size and proliferation markers. Recently, the role of the follicle-stimulating hormone receptor (FSHR) from the clinical perspective has also been increasingly discussed. The FSHR is expressed in the endothelial cells of both intratumoral and peritumoral blood vessels, where it contributes to neoangiogenesis and blood vessel remodeling. FSHR expression is associated with a range of tumor types, such as gastrointestinal tumors, and it is not detected in healthy tissues located more than 10 mm from the tumor site or in tumor lymphatics. In this study, we evaluated the expression of FSHR and CD31 in the blood vessels of ANENs in females and males with confirmed histopathology. We conducted a quantitative analysis of the immunohistochemical reactions and found a higher number of microvessels in the mucosa and submucosa of neuroendocrine tumors in the appendix. A higher level of FSHR expression was observed in women. Future research should consider whether an elevated number of blood vessels along with a strong pattern of FSHR expression may influence future treatment strategies.

The Latest Advancements in Diagnostic Role of Endosonography of Pancreatic Lesions

Endosonography, a minimally invasive imaging technique, has revolutionized the diagnosis and management of pancreatic diseases. This comprehensive review highlights the latest advancements in endosonography of the pancreas, focusing on key technological developments, procedural techniques, clinical applications and additional techniques, which include real-time elastography endoscopic ultrasound, contrast-enhanced-EUS, EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. EUS is well established for T-staging and N-staging of pancreaticobiliary malignancies, for pancreatic cyst discovery, for identifying subepithelial lesions (SEL), for differentiation of benign pancreaticobiliary disorders or for acquisition of tissue by EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. This review briefly describes principles and application of EUS and its related techniques.

Prediction of Pathological Grades of Pancreatic Neuroendocrine Tumors Based on Dynamic Contrast-Enhanced Ultrasound Quantitative Analysis

Objective: To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). Methods: This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C−1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C−1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. Results: A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki−67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Conclusions: Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Contrast Enhanced EUS for Predicting Solid Pancreatic Neuroendocrine Tumor Grade and Aggressiveness

Pancreatic neuroendocrine tumor (PNET) behavior assessment is a daily challenge for physicians. Modern PNET management varies from a watch-and-wait strategy to surgery depending on tumor aggressiveness. Therefore, the aggressiveness definition plays a pivotal role in the PNET work-up. The aggressiveness of PNETs is mainly based on the dimensions and histological grading, with sometimes a lack of specificity and sensibility. In the last twenty years, EUS has become a cornerstone in the diagnostic phase of PNET management for its high diagnostic yield and the possibility of obtaining a histological specimen. The number of EUS applications in the PNET work-up has been rapidly increasing with new and powerful possibilities. The application of contrast has led to an important step in PNET detection; in recent years, it has been gaining interesting applications in aggressiveness assessment. In this review, we underline the latest experiences and opportunities in the behavior assessment of PNETs using contact-enhanced EUS and contested enhanced harmonic EUS with a particular focus on the future application and possibility that these techniques could provide.

Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features

OBJECTIVES

To predict tumor grade (G1 vs. G2/3), presence of distant metastasis (M+), metastatic lymph nodes (N+), and microvascular invasion (VI) of pancreatic neuroendocrine neoplasms (PanNEN) based on preoperative CT radiomic features (RFs), by applying a machine learning approach aimed to limit overfit.

METHODS

This retrospective study included 101 patients who underwent surgery for PanNEN; the entire population was split into training (n = 70) and validation cohort (n = 31). Based on a previously validated methodology, after tumor segmentation on contrast-enhanced CT, RFs were extracted from unenhanced CT images. In addition, conventional radiological and clinical features were combined with RFs into multivariate logistic regression models using minimum redundancy and a bootstrap-based machine learning approach. For each endpoint, models were trained and validated including only RFs (RF_model), and both (radiomic and clinicoradiological) features (COMB_model).

RESULTS

Twenty-five patients had G2/G3 tumor, 37 N+, and 14 M+ and 38 were shown to have VI. From a total of 182 RFs initially extracted, few independent radiomic and clinicoradiological features were identified. For M+ and G, the resulting models showed moderate to high performances: areas under the curve (AUC) for training/validation cohorts were 0.85/0.77 (RF_model) and 0.81/0.81 (COMB_model) for M+ and 0.67/0.72 and 0.68/0.70 for G. Concerning N+ and VI, only the COMB_model could be built, with poorer performance for N+ (AUC = 0.72/0.61) compared to VI (0.82/0.75). For all endpoints, the negative predictive value was good (≥ 0.75).

CONCLUSIONS

Combining few radiomic and clinicoradiological features resulted in presurgical prediction of histological characteristics of PanNENs. Despite the limited risk of overfit, external validations are warranted.

KEY POINTS

• Histology is the only tool currently available allowing characterization of PanNEN biological characteristics important for prognostic assessment; significant limitations to this approach exist. • Based upon preoperative contrast-enhanced CT images, a machine learning approach optimized to favor models' generalizability was successfully applied to train predictive models for tumor grading (G1 vs. G2/3), microvascular invasion, metastatic lymph nodes, and distant metastatic spread. • Moderate to high discriminative models (AUC: 0.67-0.85) based on few parameters (≤ 3) showing high negative predictive value (0.75-0.98) were generated and then successfully validated.