Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database.

OBJECTIVE: This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC). METHODS: Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients. RESULTS: 51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611. CONCLUSION: In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.

Machine Learning for Predicting Distant Metastasis of Medullary Thyroid Carcinoma Using the SEER Database.

Objectives: We aimed to establish an effective machine learning (ML) model for predicting the risk of distant metastasis (DM) in medullary thyroid carcinoma (MTC). Methods: Demographic data of MTC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Institutes of Health between 2004 and 2015 to develop six ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1-score, and area under the receiver operating characteristic curve (AUC). The association between clinicopathological characteristics and target variables was interpreted. Analyses were performed using traditional logistic regression (LR). Results: In total, 2049 patients were included and 138 developed DM. Multivariable LR showed that age, sex, tumor size, extrathyroidal extension, and lymph node metastasis were predictive features for DM in MTC. Among the six ML models, the random forest (RF) had the best predictability in assessing the risk of DM in MTC, with an accuracy, precision, recall rate, F1-score, and AUC higher than those of the traditional binary LR model. Conclusion: RF was superior to traditional LR in predicting the risk of DM in MTC and can provide a valuable reference for clinicians in decision-making.

Therapeutic experience of a pancreatic mixed serous neuroendocrine neoplasm invading peripancreatic vessels: A case report.

RATIONALE: Pancreatic mixed serous neuroendocrine neoplasm (PMSNN) is an extremely rare disease. Only a few cases on the surgical treatment of PMSNN have been reported in the literature, and it is unclear whether there is invasion of important peripancreatic vessels. PATIENT CONCERNS: We report the case of a 39-year-old female patient with PMSNN accompanied by invasion of important peripancreatic vessels. She underwent surgery and achieved satisfactory recovery. DIAGNOSIS: Abdominal enhanced CT images showed an enhanced mass with a nonenhanced cyst involving the head and body of the pancreas, which invaded important peripancreatic vessels. The lesion had been misdiagnosed and mistreated as a metastatic carcinoma before admission. INTERVENTIONS: CT 3-dimensional (3D) visualization reconstruction images showed intact peripancreatic vessels. Radical pancreatoduodenectomy was successfully performed and confirmed that the main blood vessels around the pancreas were only compressed or even wrapped by the mass, but not penetrated. OUTCOMES: The patient recovered well and was discharged on the 19th day after surgery. Pathological examination reported the diagnosis of PMSNN with the collision type combination and the well-differentiated grade 2 pancreatic neuroendocrine tumor. She was followed up for 18 months without any abnormalities. LESSONS: This case demonstrates that surgical treatment of PMSNN with invasion of peripancreatic vessels can be successful. Preoperative abdominal CT 3D visualization reconstruction is helpful in determining the degree of invasion of important peripancreatic vessels, and plays a key role in formulating an accurate surgical plan and improving patient outcome.