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Huang Y, Zhang H, Chen L, Ding Q, Chen D, Liu G, Zhang X, Huang Q, Zhang D, Weng S. Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma. Front Oncol 2024; 14:1342317. [PMID: 39346735 PMCID: PMC11427235 DOI: 10.3389/fonc.2024.1342317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/23/2024] [Indexed: 10/01/2024] Open
Abstract
Objectives This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies. Methods A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit. Conclusions Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.
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Affiliation(s)
- Yue Huang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Han Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Lingfeng Chen
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Qingzhu Ding
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Dehua Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Guozhong Liu
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiang Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Qiang Huang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Denghan Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Shangeng Weng
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Clinical Research Center for Hepatobiliary Pancreatic and Gastrointestinal Malignant Tumors Precise Treatment of Fujian Province, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
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Liao H, Yuan J, Liu C, Zhang J, Yang Y, Liang H, Jiang S, Chen S, Li Y, Liu Y. Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma. Insights Imaging 2023; 14:223. [PMID: 38129708 PMCID: PMC10739634 DOI: 10.1186/s13244-023-01553-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/28/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. RESULTS To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. CONCLUSIONS Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. CRITICAL RELEVANCE STATEMENT The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. KEY POINTS • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.
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Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China
| | - Chunhua Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Jiao Zhang
- Department of Radiology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yaying Yang
- Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Hongwei Liang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Song Jiang
- Department of Radiology, Chongqing Ping An Medical Imaging Diagnosis Center, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.
| | - Yongmei Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
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Mirza-Aghazadeh-Attari M, Madani SP, Shahbazian H, Ansari G, Mohseni A, Borhani A, Afyouni S, Kamel IR. Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2570-2584. [PMID: 37202642 DOI: 10.1007/s00261-023-03940-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.
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Affiliation(s)
- Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ali Borhani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Shadi Afyouni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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Gu Q, He M, He Y, Dai A, Liu J, Chen X, Liu P. CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma. Discov Oncol 2023; 14:16. [PMID: 36735166 PMCID: PMC9898483 DOI: 10.1007/s12672-023-00624-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
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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Affiliation(s)
- Qianbiao Gu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Mengqing He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Yaqiong He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Anqi Dai
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Jianbin Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Xiang Chen
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
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Bian Y, Zheng Z, Fang X, Jiang H, Zhu M, Yu J, Zhao H, Zhang L, Yao J, Lu L, Lu J, Shao C. Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Radiology 2023; 306:160-169. [PMID: 36066369 DOI: 10.1148/radiol.220329] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.
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Affiliation(s)
- Yun Bian
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Zhilin Zheng
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Xu Fang
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Hui Jiang
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Mengmeng Zhu
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Jieyu Yu
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Haiyan Zhao
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Ling Zhang
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Jiawen Yao
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Le Lu
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Jianping Lu
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
| | - Chengwei Shao
- From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.)
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Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer. Eur Urol Focus 2023; 9:145-153. [PMID: 36115774 DOI: 10.1016/j.euf.2022.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/26/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. OBJECTIVE To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC. DESIGN, SETTING, AND PARTICIPANTS Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS AND LIMITATIONS In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63). CONCLUSIONS A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach. PATIENT SUMMARY For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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8
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Liao H, Yang J, Li Y, Liang H, Ye J, Liu Y. One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography. Front Oncol 2022; 12:990156. [PMID: 36158647 PMCID: PMC9500296 DOI: 10.3389/fonc.2022.990156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making. Methods This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance. Results A total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists. Conclusions The AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective.
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Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Junjun Yang
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junyong Ye
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
- *Correspondence: Yanbing Liu,
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Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022; 95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.
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Affiliation(s)
- Luis Marti-Bonmati
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Department of Radiology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Leonor Cerdá-Alberich
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
| | | | | | - Eva Montalvá Orón
- Department of Surgery, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Judith Pérez Rojas
- Department of Pathology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Angel Alberich-Bayarri
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, Quibim
SL, Valencia,
Spain
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Shi L, Wang L, Wu C, Wei Y, Zhang Y, Chen J. Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging. Front Oncol 2022; 12:927077. [PMID: 35875061 PMCID: PMC9298539 DOI: 10.3389/fonc.2022.927077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 06/06/2022] [Indexed: 12/12/2022] Open
Abstract
PurposeThis study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.Materials and MethodsWe retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).ResultsSix radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.ConclusionsThe radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.
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Affiliation(s)
- Lin Shi
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Ling Wang
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Cuiyun Wu
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electric Healthcare, Hangzhou, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Junfa Chen
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
- *Correspondence: Junfa Chen,
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Yoshikawa T, Takenaka D, Ohno Y. Editorial for "MRI Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study". J Magn Reson Imaging 2022; 55:840-841. [PMID: 35104005 DOI: 10.1002/jmri.28090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 01/19/2022] [Indexed: 12/17/2023] Open
Affiliation(s)
- Takeshi Yoshikawa
- Department of Diagnostic and Interventional Radiology, Hyogo Cancer Center, Akashi, Japan
| | - Daisuke Takenaka
- Department of Diagnostic and Interventional Radiology, Hyogo Cancer Center, Akashi, Japan
| | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
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12
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Kang CM. What Is the Next in Developing Model to Predict Survival Outcomes of Resected Pancreatic Cancer? Gut Liver 2021; 15:797-798. [PMID: 34782489 PMCID: PMC8593513 DOI: 10.5009/gnl210503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Affiliation(s)
- Chang Moo Kang
- Division of HBP Surgery, Department of Surgery, Yonsei University College of Medicine, and Pancreatobiliary Cancer Center, Yonsei Cancer Center, Severance Hospital, Seoul, Korea
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