Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Zhang, Tao; Zhang, Yuehua; Liu, Xinglong; Xu, Hanyue; Chen, Chaoyue; Zhou, Xuan; Liu, Yichun; Ma, Xuelei

doi:10.3389/fonc.2020.521831

Cited by 15 publications

(15 citation statements)

References 47 publications

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“…Our study used a larger data set (137 versus 82) from multiple centers (2 versus 1) than that in the study of Zhang et al. Data and practice heterogeneity may have contributed to the slightly lower performance in our testing 45 …”

Section: Discussionmentioning

confidence: 86%

“…(a) GC-LNM study; (b) HOS-survival study; (c) ICC-ER study; (d) pNETs-grade study achieved an AUC of 0.780 compared to 0.771 from our model. Our study used a larger data set (137 versus 82) from multiple centers (2 versus 1) than that in the study of Zhang et al Data and practice heterogeneity may have contributed to the slightly lower performance in our testing 45.…”

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confidence: 86%

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Radiomics analysis combining unsupervised learning and handcrafted features: A multiple‐disease study

Wan

Yang

et al. 2021

Medical Physics

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To study and investigate the synergistic benefit of incorporating both conventional handcrafted and learning-based features in disease identification across a wide range of clinical setups. Methods and materials:In this retrospective study, we collected 170, 150, 209, and 137 patients with four different disease types associated with identification objectives : Lymph node metastasis status of gastric cancer (GC), 5-year survival status of patients with high-grade osteosarcoma (HOS), early recurrence status of intrahepatic cholangiocarcinoma (ICC), and pathological grades of pancreatic neuroendocrine tumors (pNETs). Computed tomography (CT) and magnetic resonance imaging (MRI) were used to derive image features for GC/HOS/pNETs and ICC,respectively.In each study,67 universal handcrafted features and study-specific features based on the sparse autoencoder (SAE) method were extracted and fed into the subsequent feature selection and learning model to predict the corresponding disease identification. Models using handcrafted alone, SAE alone, and hybrid features were optimized and their performance was compared. Prominent features were analyzed both qualitatively and quantitatively to generate study-specific and cross-study insight. In addition to direct performance gain assessment, correlation analysis was performed to assess the complementarity between handcrafted features and SAE features. Results: On the independent hold-off test, the handcrafted, SAE, and hybrid features based prediction yielded area under the curve of 0.

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Section: Discussionmentioning

confidence: 86%

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confidence: 86%

Radiomics analysis combining unsupervised learning and handcrafted features: A multiple‐disease study

Wan

Yang

et al. 2021

Medical Physics

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“…Plain CT has lower cost and more convenience than contrast-enhanced CT. Also, accurate preoperative TNM staging of the tumor is difficult, as the preoperative assessment of “N” and “M” status remains challenging. Interestingly, Zhang’s research ( 36 ) depicted impressive results based on enhanced CT radiomic features with 3D modeling.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

et al. 2022

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BackgroudTumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited.MethodsThe models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann–Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram.ResultsA total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908–0.914) and 0.837 (95% CI, 0.827–0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916–0.922; validation set: AUC = 0.875, 95% CI = 0.867–0.883).ConclusionsOur developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

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“…Because NETs are relatively rare tumors, only a few machine learning applications studies have focused on NETs. Most research studies have focused on disease diagnosis, such as imaging parameters ( 11 ), pathological manifestations ( 12 ), or biomarker analysis ( 13 ). The SEER registry database effectively compensates for the deficiencies in the clinical data in traditional research centers.…”

Section: Discussionmentioning

confidence: 99%

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

Cheng

et al. 2021

Front. Surg.

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Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning.Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset.Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83).Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.

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Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Cited by 15 publications

References 47 publications

Radiomics analysis combining unsupervised learning and handcrafted features: A multiple‐disease study

Radiomics analysis combining unsupervised learning and handcrafted features: A multiple‐disease study

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

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

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