Artificial Intelligence to Predict Lymph Node Metastasis at CT in                     Pancreatic Ductal Adenocarcinoma

Bian, Yun; Zheng, Zhilin; Fang, Xu; Jiang, Hui; Zhu, Mengmeng; Yu, Jieyu; Zhao, Haiyan; Zhang, Ling; Yao, Jiawen; Lü, Le; Lu, Jianping; Shao, Chengwei

doi:10.1148/radiol.220329

Cited by 61 publications

(24 citation statements)

References 27 publications

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“…The following three feature selection steps reduced overfitting: variance analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm, as have been demonstrated in previous radiomic studies ( 27 ). The sequential Bonferroni correction method was applied to adjust the baseline significance level (α = 0.05) for multiple testing biases ( 28 , 29 ).…”

Section: Methodsmentioning

confidence: 84%

Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

Liu

et al. 2023

Front. Oncol.

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PurposeTo evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis.ResultsA total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28–86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36–84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74).ConclusionsThe radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.

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Section: Methodsmentioning

confidence: 84%

Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

Liu

et al. 2023

Front. Oncol.

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“…Gao et al established a radiomics model with good performance, which was similar to Liu et al [ 11 , 12 ]. Bian et al directly focused on the lymph node itself, combined with artificial intelligence, and obtained the best performance (AUC, 0.92) [ 26 ]. However, these studies ignored the fact that pancreatic cancer is a systemic disease and the role of body composition changes in the prediction of LNM.…”

Section: Discussionmentioning

confidence: 99%

CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma

et al. 2023

Discov Onc

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Background To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC). Methods We retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient’s body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method. Results A total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively. Conclusions CT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.

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“…Wei et al 20 used a combination of machine learning and deep learning algorithms to extract features from PET/CT images to predict the difference between PDAC and autoimmune pancreatitis, developing a multi-domain fusion model with an overall performance of AUC, accuracy, sensitivity, and speci city of 0.96, 0.90, 0.88, and 0.93, respectively. Bian et al 21 developed and validated an automated preoperative AI algorithm for tumor and lymph node segmentation in CT imaging to predict LN metastasis in PDAC patients. Lee et al 22 developed a deep learning model based on clinical data to predict postoperative survival in pancreatic cancer patients, and the model's performance in predicting 2-year OS was comparable to AJCC (AUC, 0.67; P=0.35), and it was better than AJCC in predicting 1-year recurrence free survival (AUC, 0.54; P=0.049).…”

Section: Discussionmentioning

confidence: 99%

18 F-FDG-PET/CT based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery

Zhang

Bao

Liu

et al. 2023

Preprint

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Background ：The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma（PDAC）patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning（DL）model based on 18F-FDG-PET/CT for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. Results ：A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by 7-fold cross-validation.The Dice score of the developed PET/CT based tumour segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. Conclusion ：To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision making.

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Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma

Cited by 61 publications

References 27 publications

Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma

18 F-FDG-PET/CT based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery

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