Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study

Cheng, Xiaoyun; Li, Jinzhang; Xu, Tianming; Li, Kemin; Li, Jingnan

doi:10.3389/fsurg.2021.745220

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…XGBoost, a typical boosting algorithm, can adjust the errors generated by existing models, which is efficient, flexible and portable ( 22 ). These advantages ensure the superior performance of XGBoost to other models in ML competitions ( 29 ). Thus, an XGBoost model was constructed in this study and showed some predictive ability in both the training and validation sets.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Xu¹,

Lü²

2022

J Gastrointest Oncol

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Background: Nomograms have been established to predict survival in postoperative or elderly intrahepatic cholangiocarcinoma (ICC) patients. There are no models to predict postoperative survival in elderly ICC patients. Extreme gradient boosting (XGBoost) can adjust the errors generated by existing models. This retrospective cohort study aimed to develop and validate an XGBoost model to predict postoperative 5-year survival in elderly ICC patients. Methods: The Surveillance, Epidemiology, and End Results (SEER) program provided data on elderly ICC patients aged 60 years or older and undergoing surgery. The median follow-up time was 20 months. Totally 1,055 patients were classified as training (n=738) and testing (n=317) sets at a ratio of 7:3. The outcome was postoperative 5-year survival. Demographic, tumor-related and treatment-related variables were collected. Variables were screened using the XGBoost model. The predictive performance of the model was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kaplan-Meier curve. Cox regression analysis was conducted to estimate the risk of death in the predicted populations. The predictive abilities of the XGBoost model and the American Joint Commission on Cancer (AJCC) system (7th edition) were compared.Results: The XGBoost model achieved an AUC of 0.811, a sensitivity of 0.573, a specificity of 0.890, and a PPV of 0.849 in the training set. In the testing set, the model had an AUC of 0.713, a sensitivity of 0.478, a specificity of 0.814, and a PPV of 0.726. The 5-year mortality risk of patients predicted to die was 2.91 times that of patients predicted to survive [hazard ratio (HR) =2.91, 95% confidence interval (CI): 2.42-3.50]. The XGBoost model showed a better predictive performance than the AJCC staging system both in the training and testing sets. AJCC stage, multiple (satellite) tumors/nodules, tumor-node-metastasis (TNM) stage, more than one lobe invaded, direct invasion of adjacent organs, tumor size, and radiotherapy were relatively important features in survival prediction. Conclusions:The XGBoost model exhibited some predictive capacity, which may be applied to predict postoperative 5-year survival for elderly ICC patients.

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Section: Discussionmentioning

confidence: 99%

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Xu¹,

Lü²

2022

J Gastrointest Oncol

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“…They attained an AUC of 0.90, which was significantly greater than that of the American Joint Committee on Cancer (AJCC) seventh staging system. The success of this model demonstrates the utility of ML-based approaches for prognostication and guiding clinical decision-making in oncology [ 16 ]. In this study, we attempted to build an exact tool based on ML algorithms by employing a large population of patients with KC from the Surveillance, Epidemiology, and End Results (SEER) database and a real-world hospital.…”

Section: Introductionmentioning

confidence: 99%

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

Zhang

Cai

et al. 2023

International Journal of Clinical Practice

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The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.

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“…Metastasis rarely occurs in patients with a tumor diameter of <1 cm (metastasis incidence of 0%-3%). The rate of metastasis in 1-1.9 cm tumors is 10%-15% [14]. Local lymph node metastasis or liver metastasis occurs in 60%-100% of patients with a tumor size of ≥2 cm.…”

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confidence: 99%

“…Local lymph node metastasis or liver metastasis occurs in 60%-100% of patients with a tumor size of ≥2 cm. Metastatic sites include the lungs, liver, lymph nodes, bone, skull, and endocrine organs [14]. The main therapeutic strategy for rectal NETs is surgery.…”

mentioning

confidence: 99%

“…The main therapeutic strategy for rectal NETs is surgery. Due to the high heterogeneity and clonal expansion of rectal NET, 40%-50% of patients with rectal NETs have distant metastasis at the time of diagnosis [14]. Somatostatin analogs, targeted drugs, chemotherapeutic agents, and peptide receptor radionuclide therapy should be applied comprehensively in clinical practice, especially for advanced and unresectable tumors [15].…”

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confidence: 99%

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The genetic alterations of rectal neuroendocrine tumor and indications for therapy and prognosis: a systematic review

Liu

Han

et al. 2023

Endocr J

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Neuroendocrine tumors (NETs) are a type of rare tumor that can occur at multiple organs. Rectal NETs are the most common NETs in gastrointestinal tract. Due to the rarity of rectal NETs in rectal cancer, the molecular features and the correlation with patient therapeutic response and prognosis have not been investigated in detail. In this review, we focused on the molecular features, potential therapeutic targets and prognosis of rectal NETs. By summarizing the relevant studies, we established the mutational landscape of rectal NETs and identified a series of large fragment variations. Driver genes including TP53, APC, KRAS, BRAF, RB1, CDKN2A and PTEN were found as the top mutated genes. Large fragment alterations mainly involved known driver genes, including APC, TP53, CCNE1, MYC, TERT, RB1 and ATM. Germline mutations of APC, MUTYH, MSH6, MLH1 and MSH2 associated with Lynch syndrome or FAP were also found in rectal NETs. The BRAF-V600E mutation was reported as an actionable target in rectal NETs, and the combined BRAF/MEK inhibitors were found to be effective targeting BRAF-V600E in advanced or metastatic NETs. The known prognostic risk factors of rectal adenocarcinoma, including a series of demographic and clinicopathological factors were also prognostic factors for rectal NETs. Furthermore, three types of markers, including genetic alterations, protein expression levels and methylation, were also suggested as prognostic factors for rectal NETs. In summary, we established the landscape of mutations and large-fragment alterations of rectal NETs, and identified potential therapeutic targets and a series of prognostic factors. Future studies may focus on the optimization of therapeutic strategies based on potential actionable biomarkers.

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Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study

Cited by 9 publications

References 17 publications

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

The genetic alterations of rectal neuroendocrine tumor and indications for therapy and prognosis: a systematic review

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