1
|
Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
Collapse
Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Mukherjee S, Korfiatis P, Khasawneh H, Rajamohan N, Patra A, Suman G, Singh A, Thakkar J, Patnam NG, Trivedi KH, Karbhari A, Chari ST, Truty MJ, Halfdanarson TR, Bolan CW, Sandrasegaran K, Majumder S, Goenka AH. Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs. Pancreatology 2023; 23:522-529. [PMID: 37296006 PMCID: PMC10676442 DOI: 10.1016/j.pan.2023.05.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
OBJECTIVES To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.
Collapse
Affiliation(s)
- Sovanlal Mukherjee
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Panagiotis Korfiatis
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Hala Khasawneh
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Naveen Rajamohan
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Anurima Patra
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Garima Suman
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Aparna Singh
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Jay Thakkar
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Nandakumar G Patnam
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Kamaxi H Trivedi
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Aashna Karbhari
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
| | - Mark J Truty
- Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | | | - Candice W Bolan
- Department of Radiology, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL, 32224, USA.
| | - Kumar Sandrasegaran
- Department of Radiology, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA.
| | - Shounak Majumder
- Department of Gastroenterology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| |
Collapse
|
5
|
Huang C, Chopra S, Bolan CW, Chandarana H, Harfouch N, Hecht EM, Lo GC, Megibow AJ. Pancreatic Cystic Lesions: Next Generation of Radiologic Assessment. Gastrointest Endosc Clin N Am 2023; 33:533-546. [PMID: 37245934 DOI: 10.1016/j.giec.2023.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Pancreatic cystic lesions are frequently identified on cross-sectional imaging. As many of these are presumed branch-duct intraductal papillary mucinous neoplasms, these lesions generate much anxiety for the patients and clinicians, often necessitating long-term follow-up imaging and even unnecessary surgical resections. However, the incidence of pancreatic cancer is overall low for patients with incidental pancreatic cystic lesions. Radiomics and deep learning are advanced tools of imaging analysis that have attracted much attention in addressing this unmet need, however, current publications on this topic show limited success and large-scale research is needed.
Collapse
Affiliation(s)
- Chenchan Huang
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Avenue, 3F, New York, NY 10016, USA.
| | - Sumit Chopra
- Department of Radiology, NYU Grossman School of Medicine, 650 First Avenue, 4th Floor, New York, NY 10016, USA
| | - Candice W Bolan
- Department of Radiology, Mayo Clinic in Florida, 4500 San Pablo Road South, Jacksonville, FL 32224, USA
| | - Hersh Chandarana
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Avenue, 3F, New York, NY 10016, USA
| | - Nassier Harfouch
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Avenue, 3F, New York, NY 10016, USA
| | - Elizabeth M Hecht
- Department of Radiology, New York Presbyterian - Weill Cornell Medicine, 520 East 70th Street, Starr 8a, New York, NY 10021, USA
| | - Grace C Lo
- Department of Radiology, New York Presbyterian - Weill Cornell Medicine, 520 East 70th Street, Starr 7a, New York, NY 10021, USA
| | - Alec J Megibow
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Avenue, 3F, New York, NY 10016, USA
| |
Collapse
|
6
|
Yao J, Cao K, Hou Y, Zhou J, Xia Y, Nogues I, Song Q, Jiang H, Ye X, Lu J, Jin G, Lu H, Xie C, Zhang R, Xiao J, Liu Z, Gao F, Qi Y, Li X, Zheng Y, Lu L, Shi Y, Zhang L. Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study. Ann Surg 2023; 278:e68-e79. [PMID: 35781511 DOI: 10.1097/sla.0000000000005465] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. BACKGROUND Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. METHODS This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. RESULTS Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk. CONCLUSIONS Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.
Collapse
Affiliation(s)
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Key Laboratory of Medical Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jian Zhou
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yingda Xia
- DAMO Academy, Alibaba Group, New York, NY
| | - Isabella Nogues
- Departments of Biostatistics, Harvard University T.H. Chan School of Public Health, Boston, MA
| | - Qike Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Shanghai, China
| | - Xianghua Ye
- Department of Radiotherapy, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Gang Jin
- Department of Surgery, Changhai Hospital, Shanghai, China
| | - Hong Lu
- Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Tianjin, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Rong Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jing Xiao
- Ping An Technology Co. Ltd., Shenzhen, Guangdong, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Feng Gao
- Department of Hepato-pancreato-biliary Tumor Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yafei Qi
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xuezhou Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yang Zheng
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Le Lu
- DAMO Academy, Alibaba Group, New York, NY
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Key Laboratory of Medical Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ling Zhang
- DAMO Academy, Alibaba Group, New York, NY
| |
Collapse
|
7
|
Heiselman JS, Ecker BL, Langdon-Embry L, O’Reilly EM, Miga MI, Jarnagin WR, Do RKG, Horvat N, Wei AC, Chakraborty J. Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration. J Med Imaging (Bellingham) 2023; 10:036002. [PMID: 37274758 PMCID: PMC10237235 DOI: 10.1117/1.jmi.10.3.036002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 04/18/2023] [Accepted: 05/15/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging. Approach Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators. Results Average target registration errors were 2.56 ± 2.45 mm for a biomechanical image registration algorithm and 4.15 ± 3.63 mm for a diffeomorphic intensity-based algorithm, corresponding to 1-2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators. Conclusions Volumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.
Collapse
Affiliation(s)
- Jon S. Heiselman
- Memorial Sloan Kettering Cancer Center, Department of Surgery, Hepatopancreatobiliary Unit, New York, New York, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Brett L. Ecker
- Rutgers Cancer Institute of New Jersey, Department of Surgery, New Brunswick, New Jersey, United States
| | - Liana Langdon-Embry
- Rutgers New Jersey Medical School, Cooperman Barnabas Medical Center, Livingston, New Jersey, United States
| | - Eileen M. O’Reilly
- Memorial Sloan Kettering Cancer Center, Department of Medicine, New York, New York, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - William R. Jarnagin
- Memorial Sloan Kettering Cancer Center, Department of Surgery, Hepatopancreatobiliary Unit, New York, New York, United States
| | - Richard K. G. Do
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Natally Horvat
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Alice C. Wei
- Memorial Sloan Kettering Cancer Center, Department of Surgery, Hepatopancreatobiliary Unit, New York, New York, United States
| | - Jayasree Chakraborty
- Memorial Sloan Kettering Cancer Center, Department of Surgery, Hepatopancreatobiliary Unit, New York, New York, United States
| |
Collapse
|
8
|
Lee J, Jeon J, Hong Y, Jeong D, Jang Y, Jeon B, Baek HJ, Cho E, Shim H, Chang HJ. Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising. Comput Biol Med 2023; 159:106931. [PMID: 37116238 DOI: 10.1016/j.compbiomed.2023.106931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 04/30/2023]
Abstract
BACKGROUND Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. METHOD We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. RESULTS Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. CONCLUSION We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.
Collapse
Affiliation(s)
- Jina Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Ontact Health, Seoul, 03764, South Korea.
| | - Dawun Jeong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Yeonggul Jang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Byunghwan Jeon
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, 17035, South Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, 51472, South Korea
| | - Eun Cho
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, 51472, South Korea
| | - Hackjoon Shim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, 03722, South Korea
| |
Collapse
|
9
|
Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis. Sci Rep 2023; 13:2762. [PMID: 36797285 PMCID: PMC9935887 DOI: 10.1038/s41598-022-13650-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/26/2022] [Indexed: 02/18/2023] Open
Abstract
To investigate the predictive value of radiomics based on T1-weighted contrast-enhanced MRI (CE-MRI) in forecasting the recurrence of acute pancreatitis (AP). A total of 201 patients with first-episode of acute pancreatitis were enrolled retrospectively (140 in the training cohort and 61 in the testing cohort), with 69 and 30 patients who experienced recurrence in each cohort, respectively. Quantitative image feature extraction was obtained from MR contrast-enhanced late arterial-phase images. The optimal radiomics features retained after dimensionality reduction were used to construct the radiomics model through logistic regression analysis, and the clinical characteristics were collected to construct the clinical model. The nomogram model was established by linearly integrating the clinically independent risk factor with the optimal radiomics signature. The five best radiomics features were determined by dimensionality reduction. The radiomics model had a higher area under the receiver operating characteristic curve (AUC) than the clinical model for estimating the recurrence of acute pancreatitis for both the training cohort (0.915 vs. 0.811, p = 0.020) and testing cohort (0.917 vs. 0.681, p = 0.002). The nomogram model showed good performance, with an AUC of 0.943 in the training cohort and 0.906 in the testing cohort. The radiomics model based on CE-MRI showed good performance for optimizing the individualized prediction of recurrent acute pancreatitis, which provides a reference for the prevention and treatment of recurrent pancreatitis.
Collapse
|
10
|
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: 0] [Impact Index Per Article: 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.
Collapse
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.
| | | |
Collapse
|
11
|
ABDOMEN BECKEN – Reproduzierbarkeit der radiomischen Merkmale duktaler Pankreaskarzinome. ROFO-FORTSCHR RONTG 2023. [DOI: 10.1055/a-1151-9183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
12
|
Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Am J Cancer Res 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
Collapse
Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| |
Collapse
|
13
|
Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. APPLIED SCIENCES (BASEL, SWITZERLAND) 2022; 12:9824. [PMID: 37091743 PMCID: PMC10121203 DOI: 10.3390/app12199824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
Collapse
Affiliation(s)
- Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Correspondence:
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| |
Collapse
|
14
|
Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022; 95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.
Collapse
Affiliation(s)
- Luis Marti-Bonmati
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Department of Radiology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Leonor Cerdá-Alberich
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
| | | | | | - Eva Montalvá Orón
- Department of Surgery, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Judith Pérez Rojas
- Department of Pathology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Angel Alberich-Bayarri
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, Quibim
SL, Valencia,
Spain
| |
Collapse
|
15
|
Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
Collapse
Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
16
|
Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
Collapse
|
17
|
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.
Collapse
Affiliation(s)
- Carolina de la Pinta
- Radiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain.
| |
Collapse
|
18
|
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma. Acad Radiol 2022; 30:680-688. [PMID: 35906151 DOI: 10.1016/j.acra.2022.05.019] [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: 03/18/2022] [Revised: 05/24/2022] [Accepted: 05/29/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC). METHODS A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist's diagnostic choices that were made with and without the nomogram's assistance were evaluated. RESULTS A seven-feature-combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17). CONCLUSION The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.
Collapse
|
19
|
Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
Collapse
Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
| |
Collapse
|
20
|
Ebrahimian S, Singh R, Netaji A, Madhusudhan KS, Homayounieh F, Primak A, Lades F, Saini S, Kalra MK, Sharma S. Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics. Acad Radiol 2022; 29:705-713. [PMID: 34412944 DOI: 10.1016/j.acra.2021.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/07/2021] [Accepted: 07/14/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To compare dual energy CT (DECT) quantitative metrics and radiomics for differentiating benign and malignant pancreatic lesions on contrast enhanced abdomen CT. MATERIALS AND METHODS Our study included 103 patients who underwent contrast-enhanced DECT for assessing focal pancreatic lesions at one of the two hospitals (Site A: age 68 ± 12 yrs; malignant = 41, benign = 18; Site B: age 46 ± 2 yrs; malignant = 23, benign = 21). All malignant lesions had histologic confirmation, and benign lesions were stable on follow up CT (>12 months) or had characteristic benign features on MRI. Arterial-phase, low- and high-kV DICOM images were processed with the DECT Tumor Analysis (DETA) to obtain DECT quantitative metrics such as HU, iodine and water content from a region of interest (ROI) over focal pancreatic lesions. Separately, we obtained DECT radiomics from the same ROI. Data were analyzed with multiple logistic regression and receiver operating characteristics to generate area under the curve (AUC) for best predictive variables. RESULTS DECT quantitative metrics and radiomics had AUCs of 0.98-0.99 at site A and 0.89-0.94 at site B data for classifying benign and malignant pancreatic lesions. There was no significant difference in the AUCs and accuracies of DECT quantitative metrics and radiomics from lesion rims and volumes among patients at both sites (p > 0.05). Supervised learning-based model with data from the two sites demonstrated best AUCs of 0.94 (DECT radiomics) and 0.90 (DECT quantitative metrics) for characterizing pancreatic lesions as benign or malignant. CONCLUSION Compared to complex DECT radiomics, quantitative DECT information provide a simpler but accurate method of differentiating benign and malignant pancreatic lesions.
Collapse
Affiliation(s)
- Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom court, Boston, MA 02114
| | - Ramandeep Singh
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom court, Boston, MA 02114
| | - Arjunlokesh Netaji
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Kumble Seetharama Madhusudhan
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom court, Boston, MA 02114
| | - Andrew Primak
- Siemens Medical Solutions USA Inc., Malvern, Pennsylvania
| | | | - Sanjay Saini
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom court, Boston, MA 02114
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom court, Boston, MA 02114.
| | - Sanjay Sharma
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| |
Collapse
|
21
|
Duan J, Qiu Q, Zhu J, Shang D, Dou X, Sun T, Yin Y, Meng X. Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images. Front Oncol 2022; 12:881931. [PMID: 35494061 PMCID: PMC9047864 DOI: 10.3389/fonc.2022.881931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Accurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images. Materials and Methods For this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction. Results For tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (P<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features. Conclusion Delineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.
Collapse
Affiliation(s)
- Jinghao Duan
- School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, China
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qingtao Qiu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jian Zhu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Dongping Shang
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xue Dou
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Tao Sun
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiangjuan Meng
- Department of Clinical Laboratory, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital and Institute, Jinan, China
- *Correspondence: Xiangjuan Meng,
| |
Collapse
|
22
|
Abdali SH, Afzali F, Baseri S, Abdalvand N, Abdollahi H. Bone radiomics reproducibility: a three-centered study on the impacts of image contrast, edge enhancement, and latitude variations. Phys Eng Sci Med 2022; 45:497-511. [PMID: 35389137 DOI: 10.1007/s13246-022-01116-4] [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: 08/02/2021] [Accepted: 03/01/2022] [Indexed: 11/25/2022]
Abstract
This study aims to measure the reproducibility of radiomics features in ankle bone radiography over changes in post-processing parameters including contrast, edge enhancement and latitude. Lateral ankle bone radiographies for sixty patients were obtained from three digital radiology centers. All images were acquired by same image acquisition settings. A two-dimensional region of interest was drawn in any image and 93 features from 6 feature sets including first and second order were extracted. The coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated to assess feature reproducibility for each center and among all centers in three scenarios: Adams (Nat Rev Endocrinol 9(1):28, 2013) ten different contrast Brown et al. (J Med Imaging 5(1):011017, 2018) ten different edge enhancement and Hirvasniemi et al. (Osteoarthr Cartilage 27(6):906-914, 2019) ten different image latitude parameters. Based on ICC analysis, it is observed that 46-100-44% of Histogram, 54-72-42% of GLCM, 43-76-36% of GLDM, 60-90-17% of GLRLM, 33-19-21% of GLSZM and 13-20-0% of NGTDM radiomics features had 90% < ICC < 100% over changes in contrast-edge enhancement-latitude changes respectively. Based on COV, GLRLM was only feature set that 100% of their features had COV ≤ 5% over changes in contrast and edge enhancement. The results presented here, indicating that radiomics features extracted are vulnerable over changes in contrast, edge enhancement and latitude. The most reproducible features that introduced in this study could be used for further clinical decision making.
Collapse
Affiliation(s)
- Seyed Hamid Abdali
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Firoozeh Afzali
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeid Baseri
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, P.O. Box: 15785 - 6171, Junction of Shahid Hemmat & Shahid Chamran Expressways, 14496, Tehran, Iran.
| | - Hamid Abdollahi
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran.,Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
23
|
Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
Collapse
Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
| |
Collapse
|
24
|
Ibrahim A, Barufaldi B, Refaee T, Silva Filho TM, Acciavatti RJ, Salahuddin Z, Hustinx R, Mottaghy FM, Maidment ADA, Lambin P. MaasPenn Radiomics Reproducibility Score: A Novel Quantitative Measure for Evaluating the Reproducibility of CT-Based Handcrafted Radiomic Features. Cancers (Basel) 2022; 14:cancers14071599. [PMID: 35406372 PMCID: PMC8997100 DOI: 10.3390/cancers14071599] [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: 01/24/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging acquisition and reconstruction parameters. However, to date, these effects have not been understood or quantified. In this study, we analyzed a significantly large number of scenarios in an effort to quantify the effects of variations on the reproducibility of HRFs. In addition, we assessed the performance of ComBat harmonization in each of the 31,375 investigated scenarios. We developed a novel score that can be considered the first attempt to objectively assess the number of reproducible HRFs in different scenario. Following further validation, the score could be used to decide on the inclusion of data acquired differently, as well as the assessment of the generalizability of developed radiomic signatures. Abstract The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging parameters, which significantly affect the generalizability of developed signatures and translation to clinical practice. However, the collective effect of the variations in imaging parameters on the reproducibility of HRFs remains unclear, with no objective measure to assess it in the absence of reproducibility analysis. We assessed these effects of variations in a large number of scenarios and developed the first quantitative score to assess the reproducibility of CT-based HRFs without the need for phantom or reproducibility studies. We further assessed the potential of image resampling and ComBat harmonization for removing these effects. Our findings suggest a need for radiomics-specific harmonization methods. Our developed score should be considered as a first attempt to introduce comprehensive metrics to quantify the reproducibility of CT-based handcrafted radiomic features. More research is warranted to demonstrate its validity in clinical contexts and to further improve it, possibly by the incorporation of more realistic situations, which better reflect real patients’ situations.
Collapse
Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU de Liege, CRC In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence:
| | - Bruno Barufaldi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Telmo M. Silva Filho
- Department of Statistics, Federal University of Paraíba, João Pessoa 58051-900, Brazil;
| | - Raymond J. Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU de Liege, CRC In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
| |
Collapse
|
25
|
Elsherif SB, Javadi S, Le O, Lamba N, Katz MHG, Tamm EP, Bhosale PR. Baseline CT-based Radiomic Features Aid Prediction of Nodal Positivity after Neoadjuvant Therapy in Pancreatic Cancer. Radiol Imaging Cancer 2022; 4:e210068. [PMID: 35333131 PMCID: PMC8965532 DOI: 10.1148/rycan.210068] [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] [Indexed: 04/26/2023]
Abstract
Purpose To study the association between CT-derived textural features of pancreatic cancer and patient outcome. Materials and Methods This retrospective study evaluated 54 patients (median age, 62 years [range, 40-88 years]; 32 men) with pancreatic cancer who underwent chemoradiation followed by surgical resection and lymph node dissection from May 2012 to June 2016. Three-dimensional segmentation of the pancreatic tumor was performed on baseline dual-energy CT images: 70-keV pancreatic parenchymal phase (PPP) images and iodine material density images. Then, 15 and 19 radiomic features were extracted from each phase, respectively. Logistic regression with elastic net regularization was used to select textural features associated with outcome, and receiver operating characteristic analysis evaluated feature performance. Survival curves were generated using the Kaplan-Meier method. Results The feature of integral total (∫ T), representing the mean intensity in Hounsfield units times the contour volume in milliliters of PPP imaging (hereafter, "∫ T (HU·mL) (PPP)"), is inversely associated with posttherapy pathologic lymph node (ypN) category. A threshold ∫ T (HU·mL) (PPP) less than 507.85 predicted ypN1-2 classification with 96% sensitivity, 34% specificity, and area under the curve of 0.61. Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased overall survival (median, 2.8 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (one event at 3.4 years) (P = .006). Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased progression-free survival (median, 1.5 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (median, 2.7 years) (P = .001). Conclusion A CT-based radiomic signature may help predict ypN category in patients with pancreatic cancer. Keywords: CT-Dual Energy, Abdomen/GI, Pancreas, Tumor Response, Outcomes Analysis © RSNA, 2022 Supplemental material is available for this article.
Collapse
|
26
|
Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study. Eur Radiol 2022; 32:4587-4595. [PMID: 35174400 PMCID: PMC9213380 DOI: 10.1007/s00330-022-08592-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/05/2022] [Accepted: 01/24/2022] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). METHODS Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75). RESULTS The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. CONCLUSIONS AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KEY POINTS • Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability. • Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging. • Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.
Collapse
|
27
|
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: 0] [Impact Index Per Article: 0] [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.
Collapse
|
28
|
Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022; 15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered. RESULTS A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. DISCUSSION Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.
Collapse
Affiliation(s)
- Calogero Casà
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Mariani
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gian Carlo Mattiucci
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
29
|
Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20:69-75. [PMID: 34816024 PMCID: PMC8591412 DOI: 10.1016/j.phro.2021.10.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation. Textural features are less robust than morphological or statistical features. A checklist is provided including items that should be reported in a radiomic study.
Purpose Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. Methods and materials Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study. Results Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features. Conclusions Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
Collapse
Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jan Bussink
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
30
|
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.
Collapse
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.
| |
Collapse
|
31
|
Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
Collapse
Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| |
Collapse
|
32
|
Do RKG, Kambadakone A. Radiomics for CT Assessment of Vascular Contact in Pancreatic Adenocarcinoma. Radiology 2021; 301:623-624. [PMID: 34491133 DOI: 10.1148/radiol.2021211635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Richard K G Do
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, H-710, New York, NY 10065 (R.K.G.D.); and Division of Abdominal Imaging and Intervention, Massachusetts General Hospital, Boston, Mass (A.K.)
| | - Avinash Kambadakone
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, H-710, New York, NY 10065 (R.K.G.D.); and Division of Abdominal Imaging and Intervention, Massachusetts General Hospital, Boston, Mass (A.K.)
| |
Collapse
|
33
|
Mahmood U, Apte A, Kanan C, Bates DDB, Corrias G, Manneli L, Oh JH, Erdi YE, Nguyen J, O'Deasy J, Shukla-Dave A. Quality control of radiomic features using 3D-printed CT phantoms. J Med Imaging (Bellingham) 2021; 8:033505. [PMID: 34222557 PMCID: PMC8240751 DOI: 10.1117/1.jmi.8.3.033505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 06/04/2021] [Indexed: 01/01/2023] Open
Abstract
Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting. Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes. Results: The structural similarity index between the segment used from the patients' DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was < 1.0 % across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with pDV > ± 15 % . Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.
Collapse
Affiliation(s)
- Usman Mahmood
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States
| | - Aditya Apte
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States
| | - Christopher Kanan
- Rochester Institute of Technology, Department of Imaging Science, Rochester, New York, United States
| | - David D B Bates
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, United States
| | - Giuseppe Corrias
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, United States
| | | | - Jung Hun Oh
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States
| | - Yusuf Emre Erdi
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States
| | | | - Joseph O'Deasy
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States
| | - Amita Shukla-Dave
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States.,Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, United States
| |
Collapse
|
34
|
Starosolski Z, Courtney AN, Srivastava M, Guo L, Stupin I, Metelitsa LS, Annapragada A, Ghaghada KB. A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:6641384. [PMID: 34220380 PMCID: PMC8216795 DOI: 10.1155/2021/6641384] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/26/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022]
Abstract
Objective Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. Materials and Methods In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal-Wallis test. Results N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Conclusions Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.
Collapse
Affiliation(s)
- Zbigniew Starosolski
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Amy N. Courtney
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Mayank Srivastava
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Linjie Guo
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Igor Stupin
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Leonid S. Metelitsa
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
| | - Ananth Annapragada
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Ketan B. Ghaghada
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| |
Collapse
|
35
|
Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner. Cancers (Basel) 2021; 13:cancers13092269. [PMID: 34066857 PMCID: PMC8125906 DOI: 10.3390/cancers13092269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Recent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect. Abstract Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.
Collapse
|
36
|
CT based radiomic approach on first line pembrolizumab in lung cancer. Sci Rep 2021; 11:6633. [PMID: 33758304 PMCID: PMC7988058 DOI: 10.1038/s41598-021-86113-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 02/24/2021] [Indexed: 02/06/2023] Open
Abstract
Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.
Collapse
|
37
|
Panda A, Korfiatis P, Suman G, Garg SK, Polley EC, Singh DP, Chari ST, Goenka AH. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset. Med Phys 2021; 48:2468-2481. [PMID: 33595105 DOI: 10.1002/mp.14782] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/07/2021] [Accepted: 02/11/2021] [Indexed: 01/24/2023] Open
Abstract
PURPOSE To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset. METHODS A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset. RESULTS Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was -2.7 cc (percent difference: -2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model-predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum-maximum DSC: 0.79 -0.96) on the NIH-PCT dataset. CONCLUSION A 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.
Collapse
Affiliation(s)
- Ananya Panda
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Garima Suman
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Sushil K Garg
- Department of Gastroenterology and Hepatology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Eric C Polley
- Department of Biostatistics, Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Dhruv P Singh
- Department of Gastroenterology and Hepatology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| |
Collapse
|
38
|
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.
Collapse
|
39
|
Wong OL, Yuan JI, Zhou Y, Yu SK, Cheung KY. Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence. Med Phys 2021; 48:1239-1249. [PMID: 33370474 DOI: 10.1002/mp.14686] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 12/14/2020] [Accepted: 12/18/2020] [Indexed: 12/19/2022] Open
Abstract
PURPOSE The purpose of this study was to quantitatively assess the longitudinal acquisition repeatability of MRI radiomics features in a three-dimensional (3D) T1-weighted (T1W) TSE sequence via a well-controlled prospective phantom study. METHODS Thirty consecutive daily datasets of an ACR-MRI phantom were acquired on two 1.5T MRI simulators using a 3D T1W TSE sequence. Images were blindly segmented by two observers. Post-acquisition processing was minimized but an intensity discretization (fixed bin size of 25). One hundred and one radiomics features (shape n = 12; first order n = 16; texture n = 73) were extracted. Longitudinal repeatability of each feature was evaluated by Pearson correlation and coefficient of variance (CV68% ). Interobserver feature value agreement was also quantified using intraclass correlation coefficient (ICC) and Bland-Altman analysis. A most repeatable radiomics feature set on both scanners was determined by feature coefficient of variance (CV68% <5%), ICC (>0.75), and the ratio of the interobserver difference to the interobserver mean δ<5%. RESULTS No trend of radiomics feature value changed with time. Longitudinal feature repeatability CV68% ranged 0.01-38.60% (mean/median: 12.5%/9.9%), and 0.01-40.47%, (8.49%/7.34%) on the scanners A and B. Shape features exhibited significantly better repeatability than first-order and texture features (all P < 0.01). Significant longitudinal repeatability difference was observed in texture features (P < 0.001) between the two scanners, but not in shape and first-order features (P > 0.30). First-order and texture features had smaller interobserver-dependent variation than acquisition-dependent variation. They also showed good interobserver agreement on both scanners (A:ICC = 0.80 ± 0.23; B:ICC = 0.80 ± 0.22), independent of acquisition repeatability. The repeatable radiomics features in common on both scanners, including 12 shape features, 0 first-order features, and 3 texture features, were determined as the most repeatable MRI radiomics feature set. CONCLUSIONS Radiomics features exhibited heterogeneous longitudinal repeatability, while the shape features were the most repeatable, in this phantom study with a 3D T1W TSE acquisition. The most repeatable radiomics feature set derived in this study should be helpful for the selection of reliable radiomics features in the future clinical use.
Collapse
Affiliation(s)
- Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - JIng Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| |
Collapse
|
40
|
Rizzetto F, Calderoni F, De Mattia C, Defeudis A, Giannini V, Mazzetti S, Vassallo L, Ghezzi S, Sartore-Bianchi A, Marsoni S, Siena S, Regge D, Torresin A, Vanzulli A. Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases. Eur Radiol Exp 2020; 4:62. [PMID: 33169295 PMCID: PMC7652946 DOI: 10.1186/s41747-020-00189-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/13/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). METHODS After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. RESULTS The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78-0.89) and Hausdorff distance (0.21 mm, 0.14-0.31 mm). Comparing RF values, MRC ranged 0-752% for 2D and 0-1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. CONCLUSIONS A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.
Collapse
Affiliation(s)
- Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Francesca Calderoni
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Arianna Defeudis
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Simone Mazzetti
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Lorenzo Vassallo
- Radiology Unit, SS Annunziata Hospital ASLCN1 Cuneo, via Ospedali 14, 12038, Cuneo, Savigliano, Italy
| | - Silvia Ghezzi
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Andrea Sartore-Bianchi
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Silvia Marsoni
- Precision Oncology, IFOM - The FIRC Institute of Molecular Oncology, via Adamello 16, 20139, Milan, Italy
| | - Salvatore Siena
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Daniele Regge
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
| |
Collapse
|
41
|
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.
Collapse
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
| |
Collapse
|
42
|
Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol 2020; 133:109371. [PMID: 33126173 DOI: 10.1016/j.ejrad.2020.109371] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate the reproducibility of textural features of pancreatic neuroendocrine neoplasms (PNENs), obtained under various CT-scanning conditions. METHODS AND MATERIALS We included 12 patients with PNENs and 2 contrast enhanced CT (CECT): 1) from our center according to standard CT-protocol; 2) from another institution. Two radiologists independently segmented the entire neoplasm volume using a 3D region of interest by LIFEx application on the arterial phase and then copied it to the other phases. 52 texture features were calculated for each phase. As a criterion for the segmentation consistency, a value of neoplasm volume was compared using the Bland-Altman method. The Kendall concordance coefficient was calculated to assess the texture features reproducibility in three scenarios: 1) different radiologists, same CECT; 2) same radiologist, different CECT; 3) different radiologists, different CECT. RESULTS For the scenario 1 the neoplasm volumes (except one large PNEN) were found within two standard deviations; this indicates high consistency of the segmentation. For the first scenario, Kendall's coefficient exceeded a threshold of 0.7 for all 52 features for all CT phases. For the second and third scenario, the concordance coefficient exceeded a threshold of 0.7 in 38, 28, 42, 45 and in 36, 25, 36, 44 features for the native, arterial, venous and delayed phases, respectively. CONCLUSION The highest reproducibility was found in the first scenario compared to the second and third: 100 % vs. 74 % and 67 %. Reproducible texture features can be reliably used to assess the PNENs structure.
Collapse
|
43
|
CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020; 38:1111-1124. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
Collapse
|
44
|
Alis D, Yergin M, Asmakutlu O, Topel C, Karaarslan E. The influence of cardiac motion on radiomics features: radiomics features of non-enhanced CMR cine images greatly vary through the cardiac cycle. Eur Radiol 2020; 31:2706-2715. [PMID: 33051731 DOI: 10.1007/s00330-020-07370-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/02/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The cardiac cycle might impair the reproducibility of radiomics features of cardiac magnetic resonance (CMR) cine images, yet this issue has not been addressed in the previous research. We aim to evaluate whether radiomics features of CMR cine images vary during the cardiac cycle and investigate the reproducibility of radiomics features of CMR cine images. METHODS This retrospective study enrolled 59 healthy adults who underwent CMR examination. Two observers segmented the myocardium on a 4D stack of three consecutive mid-ventricular short-axis cine images covering the cardiac cycle. A total of 352 radiomics features were extracted. The coefficient of variation and intraclass correlation coefficient were used to assess the feature variability through the cycle and inter-observer reproducibility, respectively. RESULTS Approximately 55% of radiomics features showed large variability through the cardiac cycle. The original features showed more variability than the Laplacian of Gaussian-filtered features (73.8% vs. 48%). The features of 4D stack cine images had a higher proportion of reproducible features (92.0%, 87.7%, and 76.1%) compared with the end-diastolic (77.8%, 62.2%, and 41.7%) and the end-systolic images (81.5%, 74.1%, and 58.8%) for intraclass correlation cut-off values of 30.80, > 0.85, and > 0.90, respectively. CONCLUSIONS Radiomics features of CMR cine images greatly vary during the cardiac cycle. The radiomics features of 4D stack of cine images are more robust compared with end-diastolic and end-systolic cine images in terms of reproducibility. The impact of the cardiac cycle on the reproducibility of the features should be considered when employing CMR cine images radiomics. KEY POINTS • There is limited evidence on the impact of cardiac motion on radiomics features of CMR cine images and the reproducibility of the radiomics features of CMR cine images. • Radiomics features of non-enhanced CMR cine images greatly vary during the cardiac cycle, and the number of "reproducible" features shows significant variations according to the cardiac phases. • The impact of cardiac cycle on the reproducibility of the radiomics features should be considered when employing CMR cine images radiomics.
Collapse
Affiliation(s)
- Deniz Alis
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey.
| | - Mert Yergin
- Department of Software Engineering and Applied Sciences, Bahcesehir University, Istanbul, Turkey
| | - Ozan Asmakutlu
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Cagdas Topel
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Ercan Karaarslan
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey
| |
Collapse
|
45
|
Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Pancreatic Cancer Imaging: A New Look at an Old Problem. Curr Probl Diagn Radiol 2020; 50:540-550. [PMID: 32988674 DOI: 10.1067/j.cpradiol.2020.08.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.
Collapse
Affiliation(s)
- Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Seyoun Park
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Ralph H Hruban
- Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| |
Collapse
|
46
|
Chu LC, Solmaz B, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls. Abdom Radiol (NY) 2020; 45:2469-2475. [PMID: 32372206 DOI: 10.1007/s00261-020-02556-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
PURPOSE The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls. MATERIALS AND METHODS In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. 3D volume of the pancreas was manually segmented from preoperative CT scans. Four hundred and seventy-eight radiomics features were extracted using in-house radiomics software. Eight hundred and fifty-four radiomics features were extracted using a commercially available research prototype. Random forest classifier was used for binary classification of PDAC vs. normal pancreas. Accuracy, sensitivity, and specificity of commercially available radiomics software were compared to in-house software. RESULTS When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house software decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged. CONCLUSION Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.
Collapse
|
47
|
Haarburger C, Müller-Franzes G, Weninger L, Kuhl C, Truhn D, Merhof D. Radiomics feature reproducibility under inter-rater variability in segmentations of CT images. Sci Rep 2020; 10:12688. [PMID: 32728098 PMCID: PMC7391354 DOI: 10.1038/s41598-020-69534-6] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/10/2020] [Indexed: 12/16/2022] Open
Abstract
Identifying image features that are robust with respect to segmentation variability is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyse radiomics feature reproducibility in two phases: first with manual segmentations provided by four expert readers and second with probabilistic automated segmentations using a recently developed neural network (PHiseg). We test feature reproducibility on three publicly available datasets of lung, kidney and liver lesions. We find consistent results both over manual and automated segmentations in all three datasets and show that there are subsets of radiomic features which are robust against segmentation variability and other radiomic features which are prone to poor reproducibility under differing segmentations. By providing a detailed analysis of robustness of the most common radiomics features across several datasets, we envision that more reliable and reproducible radiomic models can be built in the future based on this work.
Collapse
Affiliation(s)
- Christoph Haarburger
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany.
| | | | - Leon Weninger
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Daniel Truhn
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| |
Collapse
|
48
|
Steinacker JP, Steinacker-Stanescu N, Ettrich T, Kornmann M, Kneer K, Beer A, Beer M, Schmidt SA. Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy. Visc Med 2020; 37:77-83. [PMID: 33718486 DOI: 10.1159/000506656] [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: 03/24/2019] [Accepted: 02/17/2020] [Indexed: 12/20/2022] Open
Abstract
Purpose Imaging in pancreatic cancer is a challenge, especially regarding therapy response evaluation. Tumor size, attenuation, and perfusion are widely used as parameters for computed tomography (CT) examinations, but are often limited due to blurry tumor borders and missing qualitative parameters. To improve monitoring of therapy response, we tested a new CT-based approach of tumor heterogeneity feature analysis. Methods A total of 13 patients with pancreatic adenocarcinoma undergoing abdominal CT according to standard as baseline imaging with clinical follow-up and imaging (median time span 64 days) under systematic therapy (FOLFIRINOX/gemcitabine) were retrospectively analyzed. Progression was defined as new lesions and local tumor spread. Tumor heterogeneity analysis was performed using mintLesion®. Seven different image features referring to image heterogeneity were analyzed. Statistical analysis was performed with Spearman's rank correlation and Mann-Whitney U test. Results During follow-up, tumor volume did not significantly change between our groups with overall progression (local and systemic) and progression-free patients (p = 0.661). Mean positivity of pixel values were significantly higher in patients without progression compared to patients with progression (p = 0.030). There was a significant negative correlation between changes in kurtosis and time to local tumor spread (p = 0.008) or systemic progression (p = 0.017). Conclusions Results suggest that analysis of tumor heterogeneity might provide valuable information from routine-acquired images regarding therapy response evaluation. This might help adjusting therapy regimes and could be easily integrated in clinical workflows. Furthermore, this procedure might possibly predict therapy response and, hence could lead the way to find a potential marker for progression-free survival.
Collapse
Affiliation(s)
- Jochen Paul Steinacker
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | | | - Thomas Ettrich
- Department for Internal Medicine I, University Hospital Ulm, Ulm, Germany
| | - Marko Kornmann
- Department for General and Visceral Surgery, University Hospital Ulm, Ulm, Germany
| | - Katharina Kneer
- Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany
| | - Ambros Beer
- Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| |
Collapse
|