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Zhang N, He J, Maithel SK, Poultsides G, Rocha F, Weber S, Fields R, Idrees K, Cho C, Lv Y, Zhang XF, Pawlik TM. Accuracy and Prognostic Impact of Nodal Status on Preoperative Imaging for Management of Pancreatic Neuroendocrine Tumors: A Multi-Institutional Study. Ann Surg Oncol 2024; 31:2882-2891. [PMID: 38097878 DOI: 10.1245/s10434-023-14758-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/25/2023] [Indexed: 04/10/2024]
Abstract
BACKGROUND We sought to define the accuracy of preoperative imaging to detect lymph node metastasis (LNM) among patients with pancreatic neuroendocrine tumors (pNETs), as well as characterize the impact of preoperative imaging nodal status on survival. METHODS Patients who underwent curative-intent resection for pNETs between 2000 and 2020 were identified from eight centers. Sensitivity and specificity of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and OctreoScan for LNM were evaluated. The impact of preoperative lymph node status on lymphadenectomy (LND), as well as overall and recurrence-free survival was defined. RESULTS Among 852 patients, 235 (27.6%) individuals had LNM on final histologic examination (hN1). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 12.4%, 98.1%, 71.8%, and 74.4% for CT, 6.3%, 100%, 100%, and 80.1% for MRI, 9.5%, 100%, 100%, and 58.7% for PET, 11.3%, 97.5%, 66.7%, and 70.8% for OctreoScan, respectively. Among patients with any combination of these imaging modalities, overall sensitivity, specificity, PPV, and NPV was 14.9%, 97.9%, 72.9%, and 75.1%, respectively. Preoperative N1 on imaging (iN1) was associated with a higher number of LND (iN1 13 vs. iN0 9, p = 0.003) and a higher frequency of final hN1 versus preoperative iN0 (iN1 72.9% vs. iN0 24.9%, p < 0.001). Preoperative iN1 was associated with a higher risk of recurrence versus preoperative iN0 (median recurrence-free survival, iN1→hN1 47.5 vs. iN0→hN1 92.7 months, p = 0.05). CONCLUSIONS Only 4% of patients with LNM on final pathologic examine had preoperative imaging that was suspicious for LNM. Traditional imaging modalities had low sensitivity to determine nodal status among patients with pNETs.
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Affiliation(s)
- Nan Zhang
- Department of Hepatobiliary Surgery, Institute of Advanced Surgical Technology and Engineering, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jin He
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Shishir K Maithel
- Division of Surgical Oncology, Department of Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | | | - Flavio Rocha
- Department of Surgery, Oregon Health and Science University, Portland, OR, USA
| | - Sharon Weber
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ryan Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, WI, USA
| | - Kamran Idrees
- Division of Surgical Oncology, Department of Surgery, Vanderbilt University, Nashville, TN, USA
| | - Cliff Cho
- Division of Hepatopancreatobiliary and Advanced Gastrointestinal Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Yi Lv
- Department of Hepatobiliary Surgery, Institute of Advanced Surgical Technology and Engineering, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xu-Feng Zhang
- Department of Hepatobiliary Surgery, Institute of Advanced Surgical Technology and Engineering, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Division of Surgical Oncology, Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
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Ikuta S, Aihara T, Nakajima T, Yamanaka N. Predicting Pathological Response to Preoperative Chemotherapy in Pancreatic Ductal Adenocarcinoma Using Post-Chemotherapy Computed Tomography Radiomics. Cureus 2024; 16:e52193. [PMID: 38348011 PMCID: PMC10859726 DOI: 10.7759/cureus.52193] [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] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
INTRODUCTION Assessing the response to preoperative treatment in pancreatic cancer provides valuable information for guiding subsequent treatment strategies. The present study aims to develop and validate a computed tomography (CT) radiomics-based machine learning (ML) model for predicting pathological response (PR) to preoperative chemotherapy in pancreatic ductal adenocarcinoma (PDAC). METHODS Retrospective data were analyzed from 86 PDAC patients undergoing neoadjuvant or conversion chemotherapy followed by surgical resection from January 2018 to May 2023. The cohort was randomly divided into training (70%, n = 60) and testing (30%, n = 26) sets. Favorable PR was defined as Evans grade IIb or greater. Radiomic features were extracted from post-chemotherapy CT images, and dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Four ML classifiers (Light Gradient Boosting Machine (LGBM), Random Forest, AdaBoost, and Quadratic Discriminant Analysis) were evaluated for predicting a favorable PR. Model performance was primarily assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis. RESULTS Forty-one (47.7%) patients had a favorable PR. LASSO analysis on the training set identified five radiomic features. The LGBM model demonstrated the best performance, with a training AUC of 0.902 and a testing AUC of 0.923. It also exhibited the lowest Brier scores, both in training (0.136) and testing (0.135). Decision curve analysis further confirmed its clinical potential. CONCLUSION The CT radiomics-based ML model exhibited promising performance in predicting PR in PDAC after neoadjuvant/conversion chemotherapy. This suggests clinical utility in optimizing surgical candidates and timing of surgery, leading to personalized treatment strategies.
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Berbís MÁ, Godino FP, Rodríguez-Comas J, Nava E, García-Figueiras R, Baleato-González S, Luna A. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (NY) 2024; 49:322-340. [PMID: 37889265 DOI: 10.1007/s00261-023-04071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023]
Abstract
Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.
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Affiliation(s)
- M Álvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, 14960, Córdoba, Spain.
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Av. del Brillante, 106, 14012, Córdoba, Spain.
| | | | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, 29016, Málaga, Spain
| | - Roberto García-Figueiras
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Sandra Baleato-González
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain
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Qian C, Chen S, Liu L, Dou W, Hu S, Zhang H. Can texture analysis of T2-weighted MRI be used to predict extrathyroidal extension in papillary thyroid carcinoma? Medicine (Baltimore) 2023; 102:e35800. [PMID: 37932993 PMCID: PMC10627607 DOI: 10.1097/md.0000000000035800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/04/2023] [Indexed: 11/08/2023] Open
Abstract
Determining the presence of extrathyroidal extension (ETE) is important for established of different surgical protocol and postoperative patient management in patients with papillary thyroid carcinoma (PTC). The correlation relationship between texture features from T2-weighted imaging (T2WI) and ETE has not been explored extensively. This study aimed to explore the value of T2-weighted magnetic resonance imaging - based whole tumor texture analysis in predict extrathyroidal extension with PTC. In this retrospectively study, 76 patients with pathologically proven PTC were recruited, who received surgical resection and underwent preoperative thyroid magnetic resonance imaging. Based on histo-pathologically findings, patients were classified into ETE and no ETE groups. ETE group was further divided into 2 subgroups (minimal ETE and extensive ETE). Whole-tumor texture analysis was independently performed by 2 radiologists on axial T2WI images. Nine histogram and gray-level co-occurrence matrix (GLCM) texture features were automatically extracted. Univariate and multivariate analysis were performed to determine risk factors associated with ETE. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Interobserver agreement, confirmed by intraclass correlation coefficients (ICCs) ranging from 0.78 to 0.89, was excellent for texture analysis between 2 radiologists. T2WI image derived entropy, standard deviation, energy and correlation have significant difference between PTC with and without ETE (all P < .05). Among these, entropy showed the best diagnostic efficiency with the area under ROC curve of 0.837, diagnostic threshold of 5.86, diagnostic sensitivity and specificity of 81.5% and 75.6%, respectively. Additionally, the multivariate analysis revealed that high entropy was an independent risk factor of ETE (odds ratio, OR = 19.348; 95%CI, 4.578-81.760; P = .001). The findings indicate a significant association between texture features of the primary tumor based on T2WI and the presence of ETE in PTC. These results have the potential to help predict ETE preoperatively in patients with PTC, offering valuable insights for clinical decision-making.
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Affiliation(s)
- Chengjia Qian
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shan Chen
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Weiqiang Dou
- GE Healthcare, MR Research China, Beijing, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China
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Litjens G, Broekmans JPEA, Boers T, Caballo M, van den Hurk MHF, Ozdemir D, van Schaik CJ, Janse MHA, van Geenen EJM, van Laarhoven CJHM, Prokop M, de With PHN, van der Sommen F, Hermans JJ. Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head. Diagnostics (Basel) 2023; 13:3198. [PMID: 37892019 PMCID: PMC10606005 DOI: 10.3390/diagnostics13203198] [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: 08/30/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.
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Affiliation(s)
- Geke Litjens
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Joris P. E. A. Broekmans
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Tim Boers
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Marco Caballo
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Maud H. F. van den Hurk
- Department of Plastic and Reconstructive Surgery, Saint Vincent’s University Hospital, D04 T6F4 Dublin, Ireland
| | - Dilek Ozdemir
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Caroline J. van Schaik
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Markus H. A. Janse
- Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Erwin J. M. van Geenen
- Department of Gastroenterology and Hepatology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Cees J. H. M. van Laarhoven
- Department of Surgery, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Peter H. N. de With
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - John J. Hermans
- Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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Shiozaki H, Gocho T, Shirai Y, Takano Y, Ohki K, Suka M, Okamoto T, Fujioka S, Toya N, Ikegami T. A Novel Observational Strategy for Nonfunctional Pancreatic Neuroendocrine Neoplasms With Texture Analysis: A Multicenter Retrospective Study. CANCER DIAGNOSIS & PROGNOSIS 2023; 3:543-550. [PMID: 37671308 PMCID: PMC10475916 DOI: 10.21873/cdp.10253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/04/2023] [Indexed: 09/07/2023]
Abstract
Background/Aim Surgical resection is recommended for nonfunctional pancreatic neuroendocrine neoplasms (NF-pNENs). However, metastasis is rare in patients with small lesions with histological grade 1 (G1); thus, observation is an optional treatment approach for small NF-pNENs. Texture analysis (TA) is an imaging analysis mode for quantification of heterogeneity by extracting quantitative parameters from images. We retrospectively evaluated the utility of TA in predicting histological grade of resected NF-pNENs in a multicenter retrospective study. Patients and Methods The utility of TA in preoperative prediction of grade were evaluated with 29 patients treated by pancreatectomy for NF-pNEN who underwent preoperative dynamic computed tomography scan between January 1, 2013 and December 31, 2020 at three hospitals affiliated with the Jikei University School of Medicine. TA was performed with dedicated software for medical imaging processing for determining histological tumor grade using dynamic computed tomography images. Results Histological tumor grades based on the 2017 World Health Organization Classification for Pancreatic Neuroendocrine Neoplasms were grade 1, 2 and 3 in 18, 10 and one patient, respectively. Preoperative grades by TA were 1 and 2/3 in 15 and 14 patients, respectively. The sensitivity, specificity and area under the curve for TA-oriented grade 1 lesions were 1.00, 0.889 and 0.965 (95% confidence interval=0.901-1.000), respectively. Conclusion TA is useful for predicting grade 2/3 NF-pNEN and can provide a safe option for observation for patients with small grade 1 lesions.
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Affiliation(s)
- Hironori Shiozaki
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Takeshi Gocho
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
| | - Yoshihiro Shirai
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
| | - Yuki Takano
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Kazuyoshi Ohki
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Machi Suka
- Department of Public Health and Environmental Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Tomoyoshi Okamoto
- Department of Surgery, The Jikei University Daisan Hospital, Tokyo, Japan
| | - Shuichi Fujioka
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Naoki Toya
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Toru Ikegami
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
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Rigiroli F, Hoye J, Lerebours R, Lyu P, Lafata KJ, Zhang AR, Erkanli A, Mettu NB, Morgan DE, Samei E, Marin D. Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma. Eur Radiol 2023; 33:5779-5791. [PMID: 36894753 DOI: 10.1007/s00330-023-09532-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/23/2022] [Accepted: 01/29/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVE To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS Consecutive patients with PDAC who underwent surgery after neoadjuvant therapy from two academic hospitals between December 2012 and June 2018 were retrospectively included. Two radiologists performed a volumetric segmentation of PDAC and mesenteric-portal axis (MPA) using a segmentation software on CT scans before (CTtp0) and after (CTtp1) neoadjuvant therapy. Segmentation masks were resampled into uniform 0.625-mm voxels to develop task-based morphologic features (n = 57). These features aimed to assess MPA shape, MPA narrowing, changes in shape and diameter between CTtp0 and CTtp1, and length of MPA segment affected by the tumor. A Kaplan-Meier curve was generated to estimate the survival function. To identify reliable radiomic features associated with survival, a Cox proportional hazards model was used. Features with an ICC ≥ 0.80 were used as candidate variables, with clinical features included a priori. RESULTS In total, 107 patients (60 men) were included. The median survival time was 895 days (95% CI: 717, 1061). Three task-based shape radiomic features (Eccentricity mean tp0, Area minimum value tp1, and Ratio 2 minor tp1) were selected. The model showed an integrated AUC of 0.72 for prediction of survival. The hazard ratio for the Area minimum value tp1 feature was 1.78 (p = 0.02) and 0.48 for the Ratio 2 minor tp1 feature (p = 0.002). CONCLUSION Preliminary results suggest that task-based shape radiomic features can predict survival in PDAC patients. KEY POINTS • In a retrospective study of 107 patients who underwent neoadjuvant therapy followed by surgery for PDAC, task-based shape radiomic features were extracted and analyzed from the mesenteric-portal axis. • A Cox proportional hazards model that included three selected radiomic features plus clinical information showed an integrated AUC of 0.72 for prediction of survival, and a better fit compared to the model with only clinical information.
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Affiliation(s)
- Francesca Rigiroli
- Department of Radiology, Duke University Health System, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA.
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 1 Deaconess Road, Boston, MA, 02215, USA.
| | - Jocelyn Hoye
- Carl E. Ravin Advanced Imaging Laboratories, Durham, NC, USA
| | - Reginald Lerebours
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Peijie Lyu
- Department of Radiology, Duke University Health System, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China
| | - Kyle J Lafata
- Carl E. Ravin Advanced Imaging Laboratories, Durham, NC, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Anru R Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Alaattin Erkanli
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | | | - Desiree E Morgan
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Durham, NC, USA
| | - Daniele Marin
- Department of Radiology, Duke University Health System, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA
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Tomaszewska E, Świątkiewicz M, Muszyński S, Donaldson J, Ropka-Molik K, Arciszewski MB, Murawski M, Schwarz T, Dobrowolski P, Szymańczyk S, Dresler S, Bonior J. Repetitive Cerulein-Induced Chronic Pancreatitis in Growing Pigs-A Pilot Study. Int J Mol Sci 2023; 24:ijms24097715. [PMID: 37175426 PMCID: PMC10177971 DOI: 10.3390/ijms24097715] [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: 03/15/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Chronic pancreatitis (CP) is an irreversible and progressive inflammatory disease. Knowledge on the development and progression of CP is limited. The goal of the study was to define the serum profile of pro-inflammatory cytokines and the cell antioxidant defense system (superoxidase dismutase-SOD, and reduced glutathione-GSH) over time in a cerulein-induced CP model and explore the impact of these changes on selected cytokines in the intestinal mucosa and pancreatic tissue, as well as on selected serum biochemical parameters. The mRNA expression of CLDN1 and CDH1 genes, and levels of Claudin-1 and E-cadherin, proteins of gut barrier, in the intestinal mucosa were determined via western blot analysis. The study showed moderate pathomorphological changes in the pigs' pancreas 43 days after the last cerulein injection. Blood serum levels of interleukin (IL)-1-beta, IL-6, tumor necrosis factor alpha (TNF-alpha), C-reactive protein (CRP), lactate dehydrogenase (LDH), gamma-glutamyl transpeptidase (GGTP), SOD and GSH were increased following cerulein injections. IL-1-beta, IL-6, TNF-alpha and GSH were also increased in jejunal mucosa and pancreatic tissue. In duodenum, decreased mRNA expression of CDH1 and level of E-cadherin and increased D-lactate, an indicator of leaky gut, indicating an inflammatory state, were observed. Based on the current results, we can conclude that repetitive cerulein injections in growing pigs not only led to CP over time, but also induced inflammation in the intestine. As a result of the inflammation, the intestinal barrier was impaired.
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Affiliation(s)
- Ewa Tomaszewska
- Department of Animal Physiology, Faculty of Veterinary Medicine, University of Life Sciences in Lublin, 20-950 Lublin, Poland
| | - Małgorzata Świątkiewicz
- Department of Animal Nutrition and Feed Science, National Research Institute of Animal Production, 32-083 Balice, Poland
| | - Siemowit Muszyński
- Department of Biophysics, Faculty of Environmental Biology, University of Life Sciences in Lublin, 20-950 Lublin, Poland
| | - Janine Donaldson
- School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg 2193, South Africa
| | - Katarzyna Ropka-Molik
- Department of Animal Molecular Biology, National Research Institute of Animal Production, 32-083 Balice, Poland
| | - Marcin B Arciszewski
- Department of Animal Anatomy and Histology, Faculty of Veterinary Medicine, University of Life Sciences in Lublin, 20-950 Lublin, Poland
| | - Maciej Murawski
- Department of Animal Nutrition, Biotechnology and Fisheries, Faculty of Animal Science, University of Agriculture in Kraków, 30-059 Kraków, Poland
| | - Tomasz Schwarz
- Department of Animal Genetics, Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Kraków, 30-059 Kraków, Poland
| | - Piotr Dobrowolski
- Department of Functional Anatomy and Cytobiology, Faculty of Biology and Biotechnology, Maria Curie-Sklodowska University, 20-033 Lublin, Poland
| | - Sylwia Szymańczyk
- Department of Animal Physiology, Faculty of Veterinary Medicine, University of Life Sciences in Lublin, 20-950 Lublin, Poland
| | - Sławomir Dresler
- Department of Analytical Chemistry, Medical University of Lublin, 20-059 Lublin, Poland
- Department of Plant Physiology and Biophysics, Faculty of Biology and Biotechnology, Maria Curie-Skłodowska University, 20-033 Lublin, Poland
| | - Joanna Bonior
- Department of Medical Physiology, Chair of Biomedical Sciences, Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, 31-126 Kraków, Poland
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Koch V, Weitzer N, Dos Santos DP, Gruenewald LD, Mahmoudi S, Martin SS, Eichler K, Bernatz S, Gruber-Rouh T, Booz C, Hammerstingl RM, Biciusca T, Rosbach N, Gökduman A, D'Angelo T, Finkelmeier F, Yel I, Alizadeh LS, Sommer CM, Cengiz D, Vogl TJ, Albrecht MH. Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics. Cancer Imaging 2023; 23:38. [PMID: 37072856 PMCID: PMC10114410 DOI: 10.1186/s40644-023-00549-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/17/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. METHODS In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student's t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. RESULTS Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955-1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767-0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587-0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10-44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697-0.864], P = .01). CONCLUSIONS Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.
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Affiliation(s)
- Vitali Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany.
| | - Nils Weitzer
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Daniel Pinto Dos Santos
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Leon D Gruenewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Simon S Martin
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Katrin Eichler
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Simon Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Tatjana Gruber-Rouh
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Christian Booz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Renate M Hammerstingl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Teodora Biciusca
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Nicolas Rosbach
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Aynur Gökduman
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Fabian Finkelmeier
- Department of Internal Medicine, University Hospital Frankfurt, Frankfurt Am Main, Germany
| | - Ibrahim Yel
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Leona S Alizadeh
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Christof M Sommer
- Clinic of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Duygu Cengiz
- Department of Radiology, University of Koc School of Medicine, Istanbul, Turkey
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
| | - Moritz H Albrecht
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany
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10
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Amaral MJ, Oliveira RC, Donato P, Tralhão JG. Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review. Dig Dis Sci 2023:10.1007/s10620-023-07904-6. [PMID: 36988759 DOI: 10.1007/s10620-023-07904-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 02/24/2023] [Indexed: 03/30/2023]
Abstract
Pancreatic cancer is one of the most fatal malignancies, as approximately 80% of patients are at advanced stages by the time of diagnosis. The main reason for the poor overall survival is late diagnosis that is partially due to the lack of tools for early-stage detection. In addition, there are several challenges in evaluating response to treatment and predicting prognosis. In this article, we do a review of the most common pancreatic cancer biomarkers with emphasis in new and promising approaches. Liquid biopsies seem to have important clinical applications in early detection, screening, prognosis, and longitudinal monitoring of on-treatment patients. Together with biomarkers in imaging, can represent valuable alternative non-invasive tools in order to achieve a more effective management of pancreatic cancer patients.
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Affiliation(s)
- Maria João Amaral
- General Surgery Department, Centro Hospitalar e Universitário de Coimbra, Praceta Mota Pinto, 3000-075, Coimbra, Portugal.
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
| | - Rui Caetano Oliveira
- Pathology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Clinical Academic Center of Coimbra (CACC), Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR) Area of Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Paulo Donato
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Radiology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - José Guilherme Tralhão
- General Surgery Department, Centro Hospitalar e Universitário de Coimbra, Praceta Mota Pinto, 3000-075, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR) Area of Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Biophysics Institute, University of Coimbra, Coimbra, Portugal
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11
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Can Imaging Using Radiomics and Fat Fraction Analysis Detect Early Tissue Changes on Historical CT Scans in the Regions of the Pancreas Gland That Subsequently Develop Adenocarcinoma? Diagnostics (Basel) 2023; 13:diagnostics13050941. [PMID: 36900085 PMCID: PMC10001321 DOI: 10.3390/diagnostics13050941] [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: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Despite a growing number of effective therapeutic options for patients with pancreatic adenocarcinoma, the prognosis remains dismal mostly due to the late-stage presentation and spread of the cancer to other organs. Because a genomic analysis of pancreas tissue revealed that it may take years, if not decades, for pancreatic cancer to develop, we performed radiomics and fat fraction analysis on contrast-enhanced CT (CECT) scans of patients with historical scans showing no evidence of cancer but who subsequently went on to develop pancreas cancer years later, in an attempt to identify specific imaging features of the normal pancreas that may portend the subsequent development of the cancer. In this IRB-exempt, retrospective, single institution study, CECT chest, abdomen, and pelvis (CAP) scans of 22 patients who had evaluable historical imaging data were analyzed. The images from the "healthy" pancreas were obtained between 3.8 and 13.9 years before the diagnosis of pancreas cancer was established. Afterwards, the images were used to divide and draw seven regions of interest (ROIs) around the pancreas (uncinate, head, neck-genu, body (proximal, middle, and distal) and tail). Radiomic analysis on these pancreatic ROIs consisted of first order quantitative texture analysis features such as kurtosis, skewness, and fat quantification. Of all the variables tested, fat fraction in the pancreas tail (p = 0.029) and asymmetry of the histogram frequency curve (skewness) of pancreas tissue (p = 0.038) were identified as the most important imaging signatures for subsequent cancer development. Changes in the texture of the pancreas as measured on the CECT of patients who developed pancreas cancer years later could be identified, confirming the utility of radiomics-based imaging as a potential predictor of oncologic outcomes. Such findings may be potentially useful in the future to screen patients for pancreatic cancer, thereby helping detect pancreas cancer at an early stage and improving survival.
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12
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Tikhonova VS, Karmazanovsky GG, Kondratyev EV, Gruzdev IS, Mikhaylyuk KA, Sinelnikov MY, Revishvili AS. Radiomics model-based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol 2023; 33:1152-1161. [PMID: 35986774 DOI: 10.1007/s00330-022-09046-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/24/2022] [Accepted: 07/13/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To develop diagnostic radiomic model-based algorithm for pancreatic ductal adenocarcinoma (PDAC) grade prediction. METHODS Ninety-one patients with histologically confirmed PDAC and preoperative CT were divided into subgroups based on tumor grade. Two histology-blinded radiologists independently segmented lesions for quantitative texture analysis in all contrast enhancement phases. The ratio of densities of PDAC and unchanged pancreatic tissue, and relative tumor enhancement (RTE) in arterial, portal venous, and delayed phases of the examination were calculated. Principal component analysis was used for multivariate predictor analysis. The selection of predictors in the binary logistic model was carried out in 2 stages: (1) using one-factor logistic models (selection criterion was p < 0.1); (2) using regularization (LASSO regression after standardization of variables). Predictors were included in proportional odds models without interactions. RESULTS There were significant differences in 4, 16, and 8 texture features out of 62 for the arterial, portal venous, and delayed phases of the study, respectively (p < 0.1). After selection, the final diagnostic model included such radiomics features as DISCRETIZED HU standard, DISCRETIZED HUQ3, GLCM Correlation, GLZLM LZLGE for the portal venous phase of the contrast enhancement, and CONVENTIONAL_HUQ3 for the delayed phase of CT study. On its basis, a diagnostic model was built, showing AUC for grade ≥ 2 of 0.75 and AUC for grade 3 of 0.66. CONCLUSION Radiomics features vary in PDAC of different grades and increase the accuracy of CT in preoperative diagnosis. We have developed a diagnostic model, including texture features, which can be used to predict the grade of PDAC. KEY POINTS • A diagnostic algorithm based on CT texture features for preoperative PDAC grade prediction was developed. • The assumption that the scanning protocol can influence the results of texture analysis was confirmed and assessed. • Our results show that tumor differentiation grade can be assessed with sufficient diagnostic accuracy using CT texture analysis presented in this study.
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Affiliation(s)
| | - Grigory G Karmazanovsky
- A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia
- Pirogov Russian National Research Medical University, Moscow, Russia
| | | | - Ivan S Gruzdev
- A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia
| | | | - Mikhail Y Sinelnikov
- Research Institute of Human Morphology, Moscow, Russia.
- Sechenov University, Moscow, Russia.
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13
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Mahmoudi T, Radmard AR, Salehnia A, Ahmadian A, Davarpanah AH, Kafieh R, Arabalibeik H. Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images. J Biomed Phys Eng 2022; 12:655-668. [PMID: 36569560 PMCID: PMC9759639 DOI: 10.31661/jbpe.v0i0.2102-1283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 12/02/2022]
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.
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Affiliation(s)
- Tahereh Mahmoudi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Aneseh Salehnia
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Alireza Ahmadian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir H Davarpanah
- MD, Department of Radiology and Imaging Sciences, Emory University School of Medicine 1364 Clifton Rd NE Atlanta, Georgia 30322, USA
| | - Raheleh Kafieh
- PhD, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Arabalibeik
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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14
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Wang F, Zhao Y, Xu J, Shao S, Yu D. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients. Front Oncol 2022; 12:1037672. [PMID: 36518321 PMCID: PMC9742428 DOI: 10.3389/fonc.2022.1037672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2023] Open
Abstract
PURPOSE To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC). METHODS 184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis. RESULTS Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort. CONCLUSION We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.
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Affiliation(s)
- Fangqing Wang
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yuxuan Zhao
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Jianwei Xu
- Department of Pancreatic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Sai Shao
- Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Dexin Yu
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
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15
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Yang J, Liu Y, Liu S. Comment on “Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer”. World J Gastroenterol 2022; 28:6310-6313. [PMID: 36504551 PMCID: PMC9730437 DOI: 10.3748/wjg.v28.i44.6310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/26/2022] [Accepted: 11/16/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies because of its high invasiveness and metastatic potential. Computed tomography (CT) is often used as a preliminary diagnostic tool for pancreatic cancer, and it is increasingly used to predict treatment response and disease stage. Recently, a study published in World Journal of Gastroenterology reported that quantitative analysis of preoperative enhanced CT data can be used to predict postoperative overall survival in patients with PDAC. A tumor relative enhancement ratio of ≤ 0.7 indicates a higher tumor stage and poor prognosis.
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Affiliation(s)
- Jian Yang
- Central Laboratory, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
| | - Ying Liu
- Department of Medical Oncology, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
| | - Shi Liu
- Central Laboratory, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
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16
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Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis. Clin Transl Gastroenterol 2022; 14:e00548. [PMID: 36434803 PMCID: PMC9875961 DOI: 10.14309/ctg.0000000000000548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/18/2022] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION Pancreatic cancer is the third leading cause of cancer deaths among men and women in the United States. We aimed to detect early changes on computed tomography (CT) images associated with pancreatic ductal adenocarcinoma (PDAC) based on quantitative imaging features (QIFs) for patients with and without chronic pancreatitis (CP). METHODS Adults 18 years and older diagnosed with PDAC in 2008-2018 were identified. Their CT scans 3 months-3 years before the diagnosis date were matched to up to 2 scans of controls. The pancreas was automatically segmented using a previously developed algorithm. One hundred eleven QIFs were extracted. The data set was randomly split for training/validation. Neighborhood and principal component analyses were applied to select the most important features. A conditional support vector machine was used to develop prediction algorithms separately for patients with and without CP. The computer labels were compared with manually reviewed CT images 2-3 years before the index date in 19 cases and 19 controls. RESULTS Two hundred twenty-seven of 554 scans of non-CP cancer cases/controls and 70 of 140 scans of CP cancer cases/controls were included (average age 71 and 68 years, 51% and 44% females for non-CP patients and patients with CP, respectively). The QIF-based algorithms varied based on CP status. For non-CP patients, accuracy measures were 94%-95% and area under the curve (AUC) measures were 0.98-0.99. Sensitivity, specificity, positive predictive value, and negative predictive value were in the ranges of 88%-91%, 96%-98%, 91%-95%, and 94%-96%, respectively. QIFs on CT examinations within 2-3 years before the index date also had very high predictive accuracy (accuracy 95%-98%; AUC 0.99-1.00). The QIF-based algorithm outperformed manual rereview of images for determination of PDAC risk. For patients with CP, the algorithms predicted PDAC perfectly (accuracy 100% and AUC 1.00). DISCUSSION QIFs can accurately predict PDAC for both non-CP patients and patients with CP on CT imaging and represent promising biomarkers for early detection of pancreatic cancer.
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Barat M, Marchese U, Pellat A, Dohan A, Coriat R, Hoeffel C, Fishman EK, Cassinotto C, Chu L, Soyer P. Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances. Can Assoc Radiol J 2022; 74:351-361. [PMID: 36065572 DOI: 10.1177/08465371221124927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Ugo Marchese
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Digestive, Hepatobiliary and Pancreatic Surgery, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | | | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Christophe Cassinotto
- Department of Radiology, CHU Montpellier, 27037University of Montpellier, Saint-Éloi Hospital, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
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18
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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Keyl J, Kasper S, Wiesweg M, Götze J, Schönrock M, Sinn M, Berger A, Nasca E, Kostbade K, Schumacher B, Markus P, Albers D, Treckmann J, Schmid KW, Schildhaus HU, Siveke JT, Schuler M, Kleesiek J. Multimodal survival prediction in advanced pancreatic cancer using machine learning. ESMO Open 2022; 7:100555. [PMID: 35988455 PMCID: PMC9588888 DOI: 10.1016/j.esmoop.2022.100555] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/23/2022] Open
Abstract
Background Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care. Methods In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups. Results Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59). Conclusions The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis. We developed a machine-learning-based prediction model that outperforms the AJCC staging system and mGPS. Applying our model to an external validation cohort demonstrates generalizability. Explainable machine learning enables to understand the decision making of our model and identifies relevant parameters. Combining clinical, imaging and genetic data holds potential for personalized prognostication in advanced PDAC.
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Affiliation(s)
- J Keyl
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany.
| | - S Kasper
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Wiesweg
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J Götze
- Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - M Schönrock
- Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - M Sinn
- Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - A Berger
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany
| | - E Nasca
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany
| | - K Kostbade
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - B Schumacher
- Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
| | - P Markus
- Department of General Surgery and Traumatology, Elisabeth Hospital Essen, Essen, Germany
| | - D Albers
- Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
| | - J Treckmann
- Department of General, Visceral and Transplant Surgery, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
| | - K W Schmid
- Medical Faculty, University of Duisburg-Essen, Essen, Germany; Institute of Pathology, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
| | - H-U Schildhaus
- Medical Faculty, University of Duisburg-Essen, Essen, Germany; Institute of Pathology, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
| | - J T Siveke
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; Bridge Institute of Experimental Tumor Therapy (BIT), West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK) Partner site Essen, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - M Schuler
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
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Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, Savevski V. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review. Healthcare (Basel) 2022; 10:healthcare10081511. [PMID: 36011168 PMCID: PMC9408381 DOI: 10.3390/healthcare10081511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/19/2022] Open
Abstract
The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | | | - Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, E.O. Ospedali Galliera, 56321 Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, 72074 Tübingen, Germany
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy
| | | | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
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21
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de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2022; 21:356-361. [PMID: 34961674 DOI: 10.1016/j.hbpd.2021.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 02/05/2023]
Abstract
Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.
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Affiliation(s)
- Carolina de la Pinta
- Radiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain.
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22
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Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach. Healthcare (Basel) 2022; 10:healthcare10061039. [PMID: 35742090 PMCID: PMC9222599 DOI: 10.3390/healthcare10061039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/01/2023] Open
Abstract
The commonly used magnetic resonance (MRI) criteria can be insufficient for discriminating mucinous from non-mucinous pancreatic cystic lesions (PCLs). The histological differences between PCLs’ fluid composition may be reflected in MRI images, but cannot be assessed by visual evaluation alone. We investigate whether additional MRI quantitative parameters such as signal intensity measurements (SIMs) and radiomics texture analysis (TA) can aid the differentiation between mucinous and non-mucinous PCLs. Fifty-nine PCLs (mucinous, n = 24; non-mucinous, n = 35) are retrospectively included. The SIMs were performed by two radiologists on T2 and diffusion-weighted images (T2WI and DWI) and apparent diffusion coefficient (ADC) maps. A total of 550 radiomic features were extracted from the T2WI and ADC maps of every lesion. The SIMs and TA features were compared between entities using univariate, receiver-operating, and multivariate analysis. The SIM analysis showed no statistically significant differences between the two groups (p = 0.69, 0.21–0.43, and 0.98 for T2, DWI, and ADC, respectively). Mucinous and non-mucinous PLCs were successfully discriminated by both T2-based (83.2–100% sensitivity and 69.3–96.2% specificity) and ADC-based (40–85% sensitivity and 60–96.67% specificity) radiomic features. SIMs cannot reliably discriminate between PCLs. Radiomics have the potential to augment the common MRI diagnosis of PLCs by providing quantitative and reproducible imaging features, but validation is required by further studies.
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Drzał A, Jasiński K, Gonet M, Kowolik E, Bartel Ż, Elas M. MRI and US imaging reveal evolution of spatial heterogeneity of murine tumor vasculature. Magn Reson Imaging 2022; 92:33-44. [DOI: 10.1016/j.mri.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/25/2022] [Accepted: 06/02/2022] [Indexed: 11/15/2022]
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Ren S, Tang HJ, Zhao R, Duan SF, Chen R, Wang ZQ. Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma. Curr Med Sci 2022; 42:217-225. [PMID: 35089491 DOI: 10.1007/s11596-022-2535-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 06/28/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
| | - Hui-Juan Tang
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, 60126, Italy
| | - Rui Zhao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | | | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
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Chen HY, Deng XY, Pan Y, Chen JY, Liu YY, Chen WJ, Yang H, Zheng Y, Yang YB, Liu C, Shao GL, Yu RS. Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis. Front Oncol 2022; 11:745001. [PMID: 35004272 PMCID: PMC8733460 DOI: 10.3389/fonc.2021.745001] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open
Abstract
Objective To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). Materials and Methods Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. Results Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. Conclusion This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.
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Affiliation(s)
- Hai-Yan Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Xue-Ying Deng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie-Yu Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yun-Ying Liu
- Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Wu-Jie Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Hong Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yao Zheng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yong-Bo Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Cheng Liu
- Research Institute of Artificial Intelligence in Healthcare, Hangzhou YITU Healthcare Technology Co. Ltd., Hangzhou, China
| | - Guo-Liang Shao
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Combining Intravoxel Incoherent Motion Diffusion Weighted Imaging and Texture Analysis for a Nomogram to Predict Early Treatment Response to Concurrent Chemoradiotherapy in Cervical Cancer Patients. JOURNAL OF ONCOLOGY 2022; 2021:9345353. [PMID: 34976060 PMCID: PMC8720018 DOI: 10.1155/2021/9345353] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/10/2021] [Indexed: 12/30/2022]
Abstract
This study aimed to predict early treatment response to concurrent chemoradiotherapy (CCRT) by combining intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) with texture analysis (TA) for cervical cancer patients and to develop a nomogram for estimating the risk of residual tumor. Ninty-three cervical cancer patients underwent conventional MRI and IVIM-DWI before CCRT. We conducted TA using T2WI. The patients were allocated to partial response (PR) and complete response (CR) groups on the basis of posttreatment MRI. Multivariate logistic regression analysis on IVIM-DWI parameters and texture features was employed to filter the independent predictors and construct the predictive nomogram. Its discrimination and calibration performances were estimated. Multivariate analysis on the IVIM-DWI parameters showed that D and f were independent predictors (OR = 4.029 and 0.889, resp.; p < 0.05). However, the multivariate analysis on the texture features indicated that GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors (OR = 43.789, 9.774, and 23.738, resp.;p < 0.05). The combination of IVIM-DWI parameters and texture features exhibited the highest predictive performance (AUC = 0.975). The nomogram to identify the patients with high-risk residual tumors exhibited an acceptable predictive performance and stability with a C-index of 0.953. Decision curve analysis demonstrated the clinical use of the nomogram. The results demonstrate that D, f, GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors for cervical cancer. The nomogram combining IVIM-DWI parameters and texture features makes it possible to identify cervical cancer patients at a high risk of residual tumor after CCRT.
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Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions. Abdom Radiol (NY) 2022; 47:4151-4159. [PMID: 36104481 PMCID: PMC9626421 DOI: 10.1007/s00261-022-03646-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC. METHODS This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann-Whitney U test, Bonferroni-Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant. RESULTS Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, "low gray-level zone emphasis" yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC. CONCLUSION Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.
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Yang R, Chen Y, Sa G, Li K, Hu H, Zhou J, Guan Q, Chen F. CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network. Abdom Radiol (NY) 2022; 47:232-241. [PMID: 34636931 PMCID: PMC8776667 DOI: 10.1007/s00261-021-03230-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/25/2021] [Accepted: 07/26/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs. PURPOSE A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs). MATERIALS AND METHODS This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard. RESULTS Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs. CONCLUSION The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.
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Affiliation(s)
- Rong Yang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, P.R. China
| | - Yizhou Chen
- College of Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, 310023, Zhejiang Province, P.R. China
| | - Guo Sa
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, P.R. China
| | - Kangjie Li
- College of Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, 310023, Zhejiang Province, P.R. China
| | - Haigen Hu
- College of Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, 310023, Zhejiang Province, P.R. China
| | - Jie Zhou
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, P.R. China
| | - Qiu Guan
- College of Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, 310023, Zhejiang Province, P.R. China.
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, P.R. China.
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Chen X, Fu R, Shao Q, Chen Y, Ye Q, Li S, He X, Zhu J. Application of artificial intelligence to pancreatic adenocarcinoma. Front Oncol 2022; 12:960056. [PMID: 35936738 PMCID: PMC9353734 DOI: 10.3389/fonc.2022.960056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. METHODS We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. RESULTS Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. CONCLUSIONS AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.
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Affiliation(s)
- Xi Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ruibiao Fu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Shao
- Department of Surgical Ward 1, Ningbo Women and Children’s Hospital, Ningbo, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Jinhui Zhu,
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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Healy GM, Salinas-Miranda E, Jain R, Dong X, Deniffel D, Borgida A, Hosni A, Ryan DT, Njeze N, McGuire A, Conlon KC, Dodd JD, Ryan ER, Grant RC, Gallinger S, Haider MA. Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation. Eur Radiol 2021; 32:2492-2505. [PMID: 34757450 DOI: 10.1007/s00330-021-08314-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/05/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model. METHODS Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses. RESULTS The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40-5.87, p < 0.001)/5.28 (95% CI 2.35-11.86, p < 0.001), respectively, along with age 1.02 (1.01-1.04, p = 0.01)/1.02 (1.00-1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543-0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496-0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524-0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities. CONCLUSION A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings. KEY POINTS • At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging. • Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters. • Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.
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Affiliation(s)
- Gerard M Healy
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | | | - Rahi Jain
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Xin Dong
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Dominik Deniffel
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
| | - Ayelet Borgida
- Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Ali Hosni
- Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - David T Ryan
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
| | - Nwabundo Njeze
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
| | - Anne McGuire
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
| | - Kevin C Conlon
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jonathan D Dodd
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Edmund Ronan Ryan
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Robert C Grant
- Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Steven Gallinger
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
- Surgical Oncology Program, Hepatobiliary Pancreatic, University Health Network, Toronto, ON, Canada
| | - Masoom A Haider
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, University of Toronto, Toronto, ON, Canada.
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada.
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma. AJR Am J Roentgenol 2021; 217:1104-1112. [PMID: 34467768 DOI: 10.2214/ajr.20.23490] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.
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Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A. Computed tomography-based radiomics approach in pancreatic tumors characterization. LA RADIOLOGIA MEDICA 2021; 126:10.1007/s11547-021-01405-0. [PMID: 34386897 DOI: 10.1007/s11547-021-01405-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.
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Affiliation(s)
- Grigory Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
- Radiology Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - Valeriya Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Evgeny Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Amiran Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
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Khene ZE, Kokorian R, Mathieu R, Gasmi A, Nathalie RL, Solène-Florence KJ, Shariat S, de Crevoisier R, Laguerre B, Bensalah K. Metastatic clear cell renal cell carcinoma: computed tomography texture analysis as predictive biomarkers of survival in patients treated with nivolumab. Int J Clin Oncol 2021; 26:2087-2093. [PMID: 34338919 DOI: 10.1007/s10147-021-02003-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/26/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION To evaluate the value of image-based texture analysis for predicting progression-free survival (PFS) and overall survival (OS) in patients with metastatic clear cell renal carcinoma (cCCR) treated with nivolumab. METHODS This retrospective study included 48 patients with metastatic cCCR treated with nivolumab. Nivolumab was used as a second- or third-line monotherapy. Texture analysis of metastatic lesions was performed on CT scanners obtained within 1 month before treatment. Texture features related to the gray-level histogram, gray-level co-occurrence, run-length matrix features, autoregressive model features, and Haar wavelet feature were extracted. Lasso penalized Cox regression analyses were performed to identify independent predictors of PFS and OS. RESULTS Median PFS and OS were 5.7 and 13.8 months. 39 patients experienced progression and 27 died. The Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of PFS: skewness, S.2.2. Correlat and S.1.1. SumVarnc. Multivariate Cox regression analysis confirmed skewness (HR (95% CI) 1.49 [1.21-1.85], p < 0.001) as an independent predictor of PFS. Regarding OS, the Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of OS: S20SumVarnc, S22Contrast and S22Entropy. Multivariate Cox regression analysis confirmed S22Entropy (HR (95% CI) 1.68 (1.31-2.14), p < 0.001) as an independent predictor of OS. CONCLUSIONS Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that predicts oncological outcomes after starting nivolumab treatment.
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Affiliation(s)
- Zine-Eddine Khene
- Department of Urology, Rennes University Hospital, Rennes, France. .,Department of Medical Oncology, Centre Eugene Marquis, Rennes, France. .,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France.
| | - Romain Kokorian
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France
| | - Romain Mathieu
- Department of Urology, Rennes University Hospital, Rennes, France
| | - Anis Gasmi
- Department of Urology, Rennes University Hospital, Rennes, France
| | | | | | - Shahrokh Shariat
- Department of Urology, Medical University Vienna, General Hospital, Vienna, Austria
| | - Renaud de Crevoisier
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France.,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
| | - Brigitte Laguerre
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France
| | - Karim Bensalah
- Department of Urology, Rennes University Hospital, Rennes, France.,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
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Chaddad A, Sargos P, Desrosiers C. Modeling Texture in Deep 3D CNN for Survival Analysis. IEEE J Biomed Health Inform 2021; 25:2454-2462. [PMID: 32960772 DOI: 10.1109/jbhi.2020.3025901] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Radiomics has shown remarkable potential for predicting the survival outcome for various types of cancers such as pancreatic ductal adenocarcinoma (PDAC). However, to date, there has been limited research using convolutional neural networks (CNN) with radiomic methods for this task, due to their requirement for large training sets. To overcome this issue, we propose a new type of radiomic descriptor modeling the distribution of learned features with a Gaussian mixture model (GMM). These parametric features (GMM-CNN) are computed from gross tumor volumes of PDAC patients defined semi-automatically in pre-operative computed tomography (CT) scans. We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RF) to predict the survival outcome of patients with PDAC. Our experiments assess the advantage of GMM-CNN compared to employing the same 3D CNN model directly, standard radiomic (i.e., histogram, texture and shape), conditional entropy (CENT) based on 3DCNN, and clinical features (i.e., serum carbohydrate antigen 19-9 and chemotherapy neoadjuvant). Using the RF model (100 samples for training; 59 samples for validation), GMM-CNN features provided the highest area under the ROC curve (AUC) of 72.0% (p = 6.4×10-5) compared to 64.0% (p = 0.01) for the 3D CNN model output, 66.8% (p = 0.01) for standard radiomic features, 64.2% (p = 0.003) for CENT, and 57.6% (p = 0.3) for clinical variables. Our results suggest that the proposed GMM-CNN features used with a RF classifier can significantly improve the capacity to prognosticate PDAC patients prior to surgery via routinely-acquired imaging data.
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Li X, Yang L, Yuan Z, Lou J, Fan Y, Shi A, Huang J, Zhao M, Wu Y. Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection. J Transl Med 2021; 19:281. [PMID: 34193166 PMCID: PMC8243478 DOI: 10.1186/s12967-021-02955-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/19/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. METHODS Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. RESULTS Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. CONCLUSIONS By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.
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Affiliation(s)
- Xiawei Li
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Litao Yang
- Department of Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310000, Zhejiang, China
| | - Zheping Yuan
- Hessian Health Technology Co., Ltd, Beijing, 100007, China
| | - Jianyao Lou
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Yiqun Fan
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China
| | - Aiguang Shi
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Junjie Huang
- Department of Surgery, Changxing People's Hospital, Huzhou, 313100, Zhejiang, China
| | - Mingchen Zhao
- Hessian Health Technology Co., Ltd, Beijing, 100007, China
| | - Yulian Wu
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China.
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China.
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing. Med Image Anal 2021; 73:102150. [PMID: 34303891 DOI: 10.1016/j.media.2021.102150] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/08/2021] [Accepted: 06/24/2021] [Indexed: 12/15/2022]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.
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Frank V, Shariati S, Budai BK, Fejér B, Tóth A, Orbán V, Bérczi V, Kaposi PN. CT texture analysis of abdominal lesions – Part II: Tumors of the Kidney and Pancreas. IMAGING 2021. [DOI: 10.1556/1647.2021.00020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
AbstractIt has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.
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Affiliation(s)
- Veronica Frank
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Sonaz Shariati
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bence Fejér
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Ambrus Tóth
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Vince Orbán
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
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Liang L, Ding Y, Yu Y, Liu K, Rao S, Ge Y, Zeng M. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study. BMC Med Imaging 2021; 21:75. [PMID: 33902469 PMCID: PMC8077911 DOI: 10.1186/s12880-021-00605-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 04/14/2021] [Indexed: 01/13/2023] Open
Abstract
Background Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment. Methods Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan–Meier method. Results Multivariable Cox regression analysis identified the probability of T1WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T1WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T1WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T1WI_NGTDM_Strength and high-T1WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively. Conclusions Whole-tumour radiomics feature of T1WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.
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Affiliation(s)
- Liang Liang
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Ying Ding
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yiyi Yu
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yingqian Ge
- Siemens Healthineers, No. 278 Zhou Zhu Road, Pudong New District, Shanghai, 201318, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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D’Onofrio M, De Robertis R, Aluffi G, Cadore C, Beleù A, Cardobi N, Malleo G, Manfrin E, Bassi C. CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas. Cancers (Basel) 2021; 13:cancers13081843. [PMID: 33924363 PMCID: PMC8069159 DOI: 10.3390/cancers13081843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/31/2022] Open
Abstract
The aim of this study was to perform a simplified radiomic analysis of pancreatic ductal adenocarcinoma based on qualitative and quantitative tumor features and to compare the results between metastatic and non-metastatic patients. A search of our radiological, surgical, and pathological databases identified 1218 patients with a newly diagnosed pancreatic ductal adenocarcinoma who were referred to our Institution between January 2014 and December 2018. Computed Tomography (CT) examinations were reviewed analyzing qualitative and quantitative features. Two hundred eighty-eight patients fulfilled the inclusion criteria and were included in this study. Overall, metastases were present at diagnosis in 86/288 patients, while no metastases were identified in 202/288 patients. Ill-defined margins and a hypodense appearance on portal-phase images were significantly more common among patients with metastases compared to non-metastatic patients (p < 0.05). Metastatic tumors showed a significantly larger size and significantly lower arterial index, perfusion index, and permeability index compared to non-metastatic tumors (p < 0.05). In the management of pancreatic ductal adenocarcinoma, early detection and correct staging are key elements. The study of computerized tomography characteristics of pancreatic ductal adenocarcinoma showed substantial differences, both qualitative and quantitative, between metastatic and non-metastatic disease.
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Affiliation(s)
- Mirko D’Onofrio
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
- Correspondence:
| | - Riccardo De Robertis
- Department of Radiology, Ospedale Civile Maggiore, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy; (R.D.R.); (N.C.)
| | - Gregorio Aluffi
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Camilla Cadore
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Alessandro Beleù
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Nicolò Cardobi
- Department of Radiology, Ospedale Civile Maggiore, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy; (R.D.R.); (N.C.)
| | - Giuseppe Malleo
- Unit of General and Pancreatic Surgery, The Pancreas Institute, Policlinico GB Rossi, University of Verona, 37129 Verona, Italy; (G.M.); (C.B.)
| | - Erminia Manfrin
- Department of Pathology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy;
| | - Claudio Bassi
- Unit of General and Pancreatic Surgery, The Pancreas Institute, Policlinico GB Rossi, University of Verona, 37129 Verona, Italy; (G.M.); (C.B.)
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Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040588. [PMID: 33806029 PMCID: PMC8064478 DOI: 10.3390/diagnostics11040588] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
This study developed a pretreatment CT-based radiomic model of lymph node response to induction chemotherapy in locally advanced head and neck squamous cell carcinoma (HNSCC) patients. This was a single-center retrospective study of patients with locally advanced HPV+ HNSCC. Forty-one enlarged lymph nodes were found from 27 patients on pretreatment CT and were split into 3:1 training and testing cohorts. Ninety-three radiomic features were extracted. A radiomic model and a combined radiomic-clinical model predicting lymph node response to induction chemotherapy were developed using multivariable logistic regression. Median age was 57 years old, and 93% of patients were male. Post-treatment evaluation was 32 days after treatment, with a median reduction in lymph node volume of 66%. A three-feature radiomic model (minimum, skewness, and low gray level run emphasis) and a combined radiomic-clinical model were developed. The combined model performed the best, with AUC = 0.85 on the training cohort and AUC = 0.75 on the testing cohort. A pretreatment CT-based lymph node radiomic signature combined with clinical parameters was able to predict nodal response to induction chemotherapy for patients with locally advanced HNSCC.
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Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques. Abdom Radiol (NY) 2021; 46:1027-1033. [PMID: 32939634 DOI: 10.1007/s00261-020-02759-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/07/2020] [Accepted: 09/03/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To determine equivalency of multi-slice 3D CTTA and single slice 2D CTTA of pancreas adenocarcinoma. METHODS This retrospective study was research ethics board approved. Untreated pancreas adenocarcinomas were segmented on CT in 128 consecutive patients. Tumor segmentation was compared using two techniques: 3D segmentation by contouring all visible tumor in a 3D volume, and 2D segmentation using only a single axial image. First-order CTTA features including mean, minimum, maximum Hounsfield units (HU), standard deviation, skewness, kurtosis, entropy, and second-order gray-level co-occurrence matrix (GLCM) features homogeneity, contrast, correlation, entropy and dissimilarity were extracted. Median values were compared using the Mann-Whitney U test with Holm-Bonferroni correction. Kendall's Rank Correlation Tau assessed for correlation, and agreement was calculated using intraclass correlation coefficients (ICC) using a two-way model with single rating and absolute agreement. Statistical significance defined as P < 0.05. RESULTS The median values of CTTA features differed significantly between 3 and 2D segmentations for all of the evaluated features except for mean attenuation, standard deviation and skewness (P = 0.2979 each). 3D and 2D segmentations had moderate correlation for mean attenuation (R = 0.69, P < 0.01), while all other features demonstrated poor to fair correlation. Agreement between 3 and 2D segmentations was good for mean attenuation (ICC: 0.87, P < 0.01), moderate for minimum (ICC: 0.65, P < 0.01) and standard deviation (ICC: 0.56, P < 0.01), and poor for all other features. CONCLUSION While pancreas adenocarcinoma CTTA features obtained using 3D and 2D segmentation have multiple associations with clinically relevant outcomes, these segmentation techniques are likely not interchangeable other than for mean HU.
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Xing H, Hao Z, Zhu W, Sun D, Ding J, Zhang H, Liu Y, Huo L. Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics. EJNMMI Res 2021; 11:19. [PMID: 33630176 PMCID: PMC7907291 DOI: 10.1186/s13550-021-00760-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/10/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose To develop and validate a machine learning model based on radiomic features derived from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images to preoperatively predict the pathological grade in patients with pancreatic ductal adenocarcinoma (PDAC). Methods A total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative 18F-FDG PET/CT scan between May 2009 and January 2016 were included in this retrospective study. The cohort of patients was divided into two separate groups for the training (99 patients) and validation (50 patients) in chronological order. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, and the XGBoost algorithm was used to build a prediction model. Conventional PET parameters, including standardized uptake value, metabolic tumor volume, and total lesion glycolysis, were also measured. The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC). Results The prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set. Conclusion The model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative 18F-FDG PET/CT radiomics features.
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Affiliation(s)
- Haiqun Xing
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China
| | - Zhixin Hao
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China
| | - Wenjia Zhu
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China
| | - Dehui Sun
- Sinounion Healthcare Inc., Building 3-B, Zhongguancun Dong Sheng International Pioneer Park, Beijing, 100192, China
| | - Jie Ding
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Yu Liu
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China
| | - Li Huo
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Dongcheng District, Chinese Academy of Medical Science, Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China. .,Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, 100730, China.
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Liang L, Luo R, Ding Y, Liu K, Shen L, Zeng H, Ge Y, Zeng M. S100A4 overexpression in pancreatic ductal adenocarcinoma: imaging biomarkers from whole-tumor evaluation with MRI and texture analysis. Abdom Radiol (NY) 2021; 46:623-635. [PMID: 32740861 DOI: 10.1007/s00261-020-02676-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 07/15/2020] [Accepted: 07/18/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To investigate the relationship between imaging findings and S100A4 overexpression in pancreatic ductal adenocarcinoma (PDAC) and to determine imaging biomarkers of S100A4 overexpression from whole-tumor evaluation with MRI and texture analysis. METHODS A total of 60 patients with pathologically confirmed PDAC were included in the study. All patients underwent preoperative abdominal contrast-enhanced MRI examination with Magnetom Aera (Siemens Healthcare, Germany, 1.5 T) at our institute. Whole-tumor evaluation including texture analysis was performed. Sections of specimens were reviewed, and the S100A4 expression status was quantitatively evaluated. Univariate and multivariate logistic regression analyses were conducted to find imaging biomarkers that could predict S100A4 overexpression. RESULTS Twenty-four tumors (40.0%) had negative results for S100A4 overexpression, and 36 tumors (60.0%) exhibited overexpression. After univariate and multivariate analysis, distal pancreatic duct dilatation, T1WI_10th percentile and the enhancement rate difference between delayed phase (DP) and portal venous phase (PVP) were identified to predict S100A4 overexpression in PDAC independently (p = 0.009, 0.012 and 0.044), with odds ratios (ORs) of 0.102, 0.139 and 4.645, respectively. The area under the ROC curve (AUC) values were 0.715, 0.707 and 0.691. The AUC value of the proposed model was 0.877 with a sensitivity of 80.6% and specificity of 75.0%. CONCLUSION A model including distal pancreatic duct dilatation, T1WI_10th percentile and the enhancement rate difference between the DP and PVP could predict S100A4 overexpression in PDAC as imaging biomarkers.
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Chen PT, Chang D, Wu T, Wu MS, Wang W, Liao WC. Applications of artificial intelligence in pancreatic and biliary diseases. J Gastroenterol Hepatol 2021; 36:286-294. [PMID: 33624891 DOI: 10.1111/jgh.15380] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/09/2020] [Accepted: 12/12/2020] [Indexed: 12/11/2022]
Abstract
The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.
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Affiliation(s)
- Po-Ting Chen
- Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Dawei Chang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Tinghui Wu
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Ming-Shiang Wu
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.,Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Wei-Chih Liao
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.,Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
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In Vivo Optical Metabolic Imaging of Long-Chain Fatty Acid Uptake in Orthotopic Models of Triple-Negative Breast Cancer. Cancers (Basel) 2021; 13:cancers13010148. [PMID: 33466329 PMCID: PMC7794847 DOI: 10.3390/cancers13010148] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/23/2020] [Accepted: 12/31/2020] [Indexed: 12/16/2022] Open
Abstract
Simple Summary A dysregulated metabolism is a hallmark of cancer. Once understood, tumor metabolic reprogramming can lead to targetable vulnerabilities, spurring the development of novel treatment strategies. Beyond the common observation that tumors rely heavily on glucose, building evidence indicates that a subset of tumors use lipids to maintain their proliferative or metastatic phenotype. This study developed an intra-vital microscopy method to quantify lipid uptake in breast cancer murine models using a fluorescently labeled palmitate molecule, Bodipy FL c16. This work highlights optical imaging’s ability to both measure metabolic endpoints non-destructively and repeatedly, as well as inform small animal metabolic phenotyping beyond in vivo optical imaging of breast cancer alone. Abstract Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach; however, identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical tumor models to guide development of new drugs that restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo and demonstrate its relevance to study both tumor metabolic reprogramming directly, as well as the effectiveness of drugs targeting lipid metabolism. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing, transgenic, triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 min, with the signal remaining stable during the 30–80 min post-injection period. We used the fluorescence at 60 min (Bodipy60), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene, consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays providing rich metabolic information at static time points and imaging approaches visualizing metabolism in whole organs at a reduced resolution.
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Salinas-Miranda E, Khalvati F, Namdar K, Deniffel D, Dong X, Abbas E, Wilson JM, O'Kane GM, Knox J, Gallinger S, Haider MA. Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy. Can Assoc Radiol J 2020; 72:605-613. [PMID: 33151087 DOI: 10.1177/0846537120968782] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology. PURPOSE To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy. METHODS The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models. RESULTS Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, P-value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, P-value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis. CONCLUSION The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.
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Affiliation(s)
- Emmanuel Salinas-Miranda
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada.,PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Farzad Khalvati
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada.,Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, Ontario, Canada
| | - Kashayar Namdar
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada
| | - Dominik Deniffel
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada
| | - Xin Dong
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada
| | - Engy Abbas
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, Ontario, Canada
| | - Julie M Wilson
- PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Grainne M O'Kane
- PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jennifer Knox
- PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Steven Gallinger
- PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Hepatobiliary Pancreatic Surgical Oncology Program, University Health Network, Toronto, Ontario, Canada
| | - Masoom A Haider
- 90755Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada.,PanCuRx Translational Research Initiative, 90755Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, Ontario, Canada
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Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2020; 31:3447-3467. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To systematically review published studies on the use of radiomics of the pancreas. METHODS The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study. RESULTS A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003). CONCLUSIONS Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. KEY POINTS • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.
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Affiliation(s)
| | - Beau Pontre
- School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Maxim S Petrov
- School of Medicine, University of Auckland, Auckland, New Zealand.
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