OBJECTIVE: The purpose of our study was to investigate the clinical significance and prognostic role of the systemic immune-inflammation index (SII) in patients who underwent surgical resection for nonfunctioning pancreatic neuroendocrine tumors (pNETs).
METHODS: We conducted a retrospective analysis of 364 patients with nonfunctioning pNETs. The association between the SII level and clinical parameters was investigated. The receiver operating characteristic (ROC) curve was used to calculate the optimal SII value. Cox proportional hazard analysis was performed to evaluate the prognostic factors.
RESULTS: Our study included 364 patients with nonfunctioning pNETs who underwent surgery. The median age was 51.0 (43.0, 59.3), and 164 (45.1%) were male. The optimal threshold of SII determined by ROC analysis was 523.95. Higher SII levels were significantly associated with older age (p = 0.001), sex (p = 0.011), tumor size (p = 0.032), and tumor grade (p = 0.002). Recurrence was observed in 70 (19.2%) patients following a median follow-up of 98 months. Univariate analysis showed that higher SII (p < 0.0001), tumor size >4 cm (p = 0.015), and G2/G3 grade (p = 0.002) were significantly associated with disease-free survival (DFS). Multivariate analysis revealed that higher SII (HR: 7.35; 95% CI: 3.44, 15.70; p < 0.0001) and G2/G3 grade (HR: 3.11; 95% CI: 1.42, 6.82; p = 0.005) remained significantly associated with tumor recurrence. Furthermore, 46 (12.6%) patients died during the follow-up. Higher SII (HR: 8.43; 95% CI: 3.19, 22.72; p < 0.0001) and G2/G3 grade (HR: 3.16; 95% CI: 1.01, 9.86; p = 0.048) were independent predictors of overall survival (OS) by multivariate analysis.
CONCLUSIONS: In conclusion, our study revealed that a higher SII level was associated with tumor-related features (larger tumor size and advanced grade) and subsequent shorter DFS and OS in patients with nonfunctioning pNETs. These results indicated that the SII could serve as an efficient prognostic biomarker for nonfunctioning pNETs.

暂无摘要。

BACKGROUND: Pulmonary primitive neuroectodermal tumor (PNET), a member of the Ewing sarcoma family of tumors, is a rare malignancy that is associated with a grim prognosis. To date, fewer than 30 cases of pulmonary PNET have been reported. In this case report, we present the clinical details of a 12-year-old girl with pulmonary PNET who underwent surgical treatment. We also conducted an analysis and summary of other relevant studies and the surgical outcomes.
METHODS: In May 2018, a 12-year-old girl was admitted with symptoms of cough and blood-tinged phlegm. A computed tomography scan revealed a large mass, measuring 12.9 cm × 8.1 cm, in the right middle and lower lungs. A percutaneous lung biopsy confirmed poorly differentiated tumor cells with a nested growth pattern. Immunohistochemical staining demonstrated positive expression of CD99, CD56, Vimentin, and Synaptophysin. The patient was diagnosed with pulmonary PNET. Following three cycles of neoadjuvant chemotherapy, a substantial reduction in tumor volume was observed. Subsequently, the patient underwent a surgical procedure involving pneumonectomy and partial resection of the left atrium with the assistance of cardiopulmonary bypass. The patient was discharged 37 days after surgery. During a three-year follow-up period, she exhibited no signs of tumor recurrence and has successfully returned to school.
CONCLUSIONS: This case highlights the successful management of an advanced PNET with neoadjuvant chemotherapy, pneumonectomy, and partial resection of the left atrium employing cardiopulmonary bypass. The patient remained disease-free after three years. Our analysis of surgically treated cases indicates that neoadjuvant chemotherapy can contribute to improved prognoses for PNET patients. It is crucial to emphasize that complete surgical excision remains the cornerstone of treatment, underscoring the importance of surgeons considering radical surgical approaches whenever feasible for patients with pulmonary PNETs.

Non-invasive prognostic predictors for rare pancreatic neuroendocrine tumors (PNETs) are lacking. We aimed to approach the prognostic value of preoperative systemic inflammatory markers in patients with PNETs.
The clinical data of 174 patients with PNETs undergoing surgical treatment were retrospectively analyzed to explore the correlation of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and platelet to white blood cell ratio (PWR) with clinicopathological parameters and the progression of tumor after the operation. The optimal cutoff values for predictors and the area under the curve (AUC) of the receiver operating characteristic (ROC) were estimated. Univariate and multivariate Cox proportional hazards models were used to assess the relation between NLR, LMR, PLR, and progression-free survival (PFS), examined by the Kaplan-Meier and log-rank tests.
The scores of the NLR (P = 0.039) and PLR (P = 0.011) in the progression group were significantly higher than those in the progression-free group, and the LMR was significantly lower than those in the progression-free group (P = 0.001). The best cutoff values of NLR, LMR, and PLR before operation were 2.28, 4.36, and 120.91. The proportions of tumor progression in the high NLR group (P = 0.007) and high PLR group (P = 0.013) obviously increased, and the proportion of tumor development in the low LMR group was higher than that in the high LMR group (P < 0.001). The K-M survival curve showed that the progression-free survival rate was lower in the high NLR group (P = 0.004), the low LMR group (P < 0.001), and the high PLR group (P = 0.018). The results of the multivariate Cox proportional hazards model suggested that preoperative LMR (HR = 3.128, 95% CI: 1.107~8.836, P = 0.031) was an independent predictor of PFS.
The markers of systemic inflammation, especially LMR, can predict the postoperative progression of PNETs.

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the \'CBCT-only\' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains.  As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the \'CBCT-only\' mode shows promising prospects for application in esophageal cancer radiotherapy.

OBJECTIVE: To develop a nomogram to predict the aggressiveness of non-functional pancreatic neuroendocrine tumors (NF-pNETs) based on preoperative computed tomography (CT) features.
METHODS: This study included 176 patients undergoing radical resection for NF-pNETs. These patients were randomly divided into the training (n = 123) and validation sets (n = 53). A nomogram was developed based on preoperative predictors of aggressiveness of the NF-pNETs which were identified by univariable and multivariable logistic regression analysis. The aggressiveness of NF-pNETs was defined as a composite measure including G3 grading, N+, distant metastases, and/ or disease recurrence.
RESULTS: Altogether, the number of patients with highly aggressive NF-pNETs was 37 (30.08 %) and 15 (28.30 %) in the training and validation sets, respectively. Multivariable logistic regression analysis identified that tumor size, biliopancreatic duct dilatation, lymphadenopathy, and enhancement pattern were preoperative predictors of aggressiveness. Those variables were used to develop a nomogram with good concordance statistics of 0.89 and 0.86 for predicting aggressiveness in the training and validation sets, respectively. With a nomogram score of 59, patients with NF-pNETs were divided into low-aggressive and high-aggressive groups. The high-aggressive group had decreased overall survival (OS) and disease-free survival (DFS). Moreover, the nomogram showed good performance in predicting OS and DFS at 3, 5, and 10 years.
CONCLUSIONS: The nomogram integrating CT features helped preoperatively predict the aggressiveness of NF-pNETs and could potentially facilitate clinical decision-making.

暂无摘要。

BACKGROUND: We sought to define the accuracy of preoperative imaging to detect lymph node metastasis (LNM) among patients with pancreatic neuroendocrine tumors (pNETs), as well as characterize the impact of preoperative imaging nodal status on survival.
METHODS: Patients who underwent curative-intent resection for pNETs between 2000 and 2020 were identified from eight centers. Sensitivity and specificity of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and OctreoScan for LNM were evaluated. The impact of preoperative lymph node status on lymphadenectomy (LND), as well as overall and recurrence-free survival was defined.
RESULTS: Among 852 patients, 235 (27.6%) individuals had LNM on final histologic examination (hN1). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 12.4%, 98.1%, 71.8%, and 74.4% for CT, 6.3%, 100%, 100%, and 80.1% for MRI, 9.5%, 100%, 100%, and 58.7% for PET, 11.3%, 97.5%, 66.7%, and 70.8% for OctreoScan, respectively. Among patients with any combination of these imaging modalities, overall sensitivity, specificity, PPV, and NPV was 14.9%, 97.9%, 72.9%, and 75.1%, respectively. Preoperative N1 on imaging (iN1) was associated with a higher number of LND (iN1 13 vs. iN0 9, p = 0.003) and a higher frequency of final hN1 versus preoperative iN0 (iN1 72.9% vs. iN0 24.9%, p < 0.001). Preoperative iN1 was associated with a higher risk of recurrence versus preoperative iN0 (median recurrence-free survival, iN1→hN1 47.5 vs. iN0→hN1 92.7 months, p = 0.05).
CONCLUSIONS: Only 4% of patients with LNM on final pathologic examine had preoperative imaging that was suspicious for LNM. Traditional imaging modalities had low sensitivity to determine nodal status among patients with pNETs.

OBJECTIVE: To evaluate the magnetic resonance imaging (MRI) and computed tomography (CT) findings along with other surgical and pathologic features as prognosis predictors in pancreatic neuroendocrine tumors (PNETs).
METHODS: In this study, we retrospectively analyzed a clinical data pool of patients with pathologically confirmed PNETs. CT and MRI findings were evaluated as potential prediction parameters of tumor-nodes-metastases (TNM) stage and grade, using Fisher\'s exact test. Univariate and multivariate logistic regression models were used to estimate the risk factors associated with tumor recurrence after surgery. The Kaplan-Meier method and Cox proportional hazards model were used for recurrence-free survival analysis.
RESULTS: The predictors of higher TNM stages were tumor diameter, tumor boundary, distant metastases, and lymphadenopathy on CT scan. From MRI images, tumor diameter, T2-weighted image, tumor enhancement, and pancreatic duct dilatation showed statistically significant differences among TNM stages. Univariate analysis showed that American Joint Committee on Cancer (AJCC) TNM stage, World Health Organization (WHO) tumor grade, sex, smoking, and drinking were associated with tumor recurrence and disease-free survival (DFS); while tumor and metastasis also affected DFS. Multivariate survival analysis confirmed that AJCC TNM was an independent predictor after adjusting other covariates. Peripancreatic invasion and lymph node metastases as well as blurred boundary detected by CT or MRI may be independent risk factors for TNM stage and clinical outcome of PNETs.
CONCLUSIONS: TNM stage is a valuable predictor of prognosis in PNETs. Information from CT and MRI imaging can be used to determine the TNM stage, and to estimate the tumor prognosis, guide the follow-up, and avoid ineffective treatments.

Pancreatic neuroendocrine neoplasms (pNENs) are relatively rare epithelial malignancies originating from pancreatic neuroendocrine cells, pathologically classified into well-differentiated pancreatic neuroendocrine tumors (pNETs) and poorly-differentiated pancreatic neuroendocrine carcinoma (pNECs). Although they also fall under the category of pNENs, the almost entirely distinct biological characteristics and survival prognosis have caused debate among surgeons when it comes to the development of surgical intervention options, particularly for locally advanced G3 pNETs and pNECs. We present a case of 66-year-old male with nonfunctional G3 pNET, invasion of five nearby pancreatic organs and type II liver metastases. The patient achieved good outcomes after undergoing multivisceral resection and postoperative adjuvant chemotherapy. This finding helps surgeons better understand locally advanced pNENs, formulate treatment decisions systematically and confidently, and balance patient benefits and risks of surgery.