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A novel risk?scoring system for predicting lymph node metastasis of rectal neuroendocrine tumors

ABSTRACT: Abstract Aim Although rectal neuroendocrine tumors (NETs) are considered to be rare low?grade malignancies when lymph node metastasis (LNM) is present, their degree of malignancy is comparable to that of colorectal cancer (CRC). However, it remains unclear as to which patients require radical lymph node dissection. The aim of this study was to elucidate the risk factors for LNM and develop a risk?scoring system for LNM to help determine appropriate therapeutic approaches. Methods In this study, we examined 103 patients with rectal NETs who underwent local resection (n = 55) or radical resection with LN dissection (n = 48). We evaluated each pathological feature, including the depth of submucosal invasion (SM depth) and tumor budding grade. Results According to our univariate analyses and previous reports, the significant five risk factors for LNM were weighted with point values: 2 points for tumor size ? 15 mm and muscularis invasion, and 1 point each for SM depth ? 2000 µm, positive lymphovascular invasion, budding grade 3, and vertical margin. The area under the receiver operating curve for the scoring system was 0.899 (95% CI: 0.843?0.955). When a score of 2 was used as the cut?off value, the sensitivity and specificity for the prediction of LNM were 100% and 72.1%, respectively. Conclusions The risk?scoring system for LNM of rectal NETs showed high diagnostic performance. Using this risk?scoring system, it is possible to predict the risk of LNM and thereby potentially avoid unnecessary surgery. Further prospective external validation studies should be performed. The study was registered in the Japanese Clinical Trials Registry as UMIN000036658. The risk?scoring system for LNM of rectal NETs could predict the risk of LNM and thereby potentially avoid unnecessary surgery.

SUBMITTER: Chida K

PROVIDER: S-EPMC7511567 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe prognosis of patients with pancreatic neuroendocrine tumors (PanNET), the second most common type of pancreatic cancer, varies significantly, and up to 15% of patients develop metastasis. Although certain morphological characteristics of PanNETs have been associated with patient outcome, there are no available morphology-based prognostic markers. Given that current clinical histopathology markers are unable to identify high-risk PanNET patients, the development of accurate prognostic biomarkers is needed. Here, we describe a novel machine learning, multiclassification pipeline to predict the risk of metastasis using morphological information from whole tissue slides.MethodsDigital images from surgically resected tissues from 89 PanNET patients were used. Pathologist-annotated regions were extracted to train a convolutional neural network (CNN) to identify tiles consisting of PanNET, stroma, normal pancreas parenchyma, and fat. Computationally annotated cancer or stroma tiles and patient metastasis status were used to train CNN to calculate a region based metastatic risk score. Aggregation of the metastatic probability scores across the slide was performed to predict the risk of metastasis.ResultsThe ability of CNN to discriminate different tissues was high (per-tile accuracy >95%; whole slide cancer regions Jaccard index = 79%). Cancer and stromal tiles with high evaluated probability provided F1 scores of 0.82 and 0.69, respectively, when we compared tissues from patients who developed metastasis and those who did not. The final model identified low-risk (n = 76) and high-risk (n = 13) patients, as well as predicted metastasis-free survival (hazard ratio: 4.71) after adjusting for common clinicopathological variables, especially in grade I/II patients.ConclusionUsing slides from surgically resected PanNETs, our novel, multiclassification, deep learning pipeline was able to predict the risk of metastasis in PanNET patients. Our results suggest the presence of prognostic morphological patterns in PanNET tissues, and that these patterns may help guide clinical decision making.

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A novel risk?scoring system for predicting lymph node metastasis of rectal neuroendocrine tumors

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