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Wang J, Song P, Zhang M, Liu W, Zeng X, Chen N, Li Y, Wang M. A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer. Cancer Med 2024; 13:e70046. [PMID: 39171859 PMCID: PMC11339853 DOI: 10.1002/cam4.70046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC). METHODS This study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network-based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA). RESULTS The optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935-1.000) and 0.885 (95% CI: 0.731-0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC-training = 0.671, 0.873, 0.833, and 0.814, AUC-test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively. CONCLUSIONS The predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.
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Affiliation(s)
- Jing Wang
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Pujiao Song
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Meng Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Wei Liu
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Xi Zeng
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Nanshan Chen
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
| | - Yuxia Li
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Minghua Wang
- Department of Nuclear MedicineThe Affiliated Hospital of Guizhou Medical UniversityGuiyangChina
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Yan R, Qin S, Xu J, Zhao W, Xin P, Xing X, Lang N. A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study. Cancer Imaging 2024; 24:100. [PMID: 39085930 PMCID: PMC11293005 DOI: 10.1186/s40644-024-00743-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 07/24/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC. METHODS Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test. RESULTS No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05). CONCLUSIONS Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.
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Affiliation(s)
- Ruixin Yan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Siyuan Qin
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
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Russo L, Bottazzi S, Kocak B, Zormpas-Petridis K, Gui B, Stanzione A, Imbriaco M, Sala E, Cuocolo R, Ponsiglione A. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools. Eur Radiol 2024:10.1007/s00330-024-10947-6. [PMID: 39014086 DOI: 10.1007/s00330-024-10947-6] [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: 04/08/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVE To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
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Affiliation(s)
- Luca Russo
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Bottazzi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Konstantinos Zormpas-Petridis
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evis Sala
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
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Mebratie DY, Dagnaw GG. Review of immunohistochemistry techniques: Applications, current status, and future perspectives. Semin Diagn Pathol 2024; 41:154-160. [PMID: 38744555 DOI: 10.1053/j.semdp.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/16/2024]
Abstract
The Hematoxylin and Eosin stain is a cornerstone in histopathology that facilitates the microscopic examination of tissue samples for identifying infections and tumors. However, challenges arise from the similar appearances of diseases and cells, prompting the emergence of Immunohistochemistry (IHC) as an important technique. This review summarizes the principles, procedures, and applications and future perspectives of IHC, a prevalent immunostaining method allowing the detection of specific proteins in tissue sections. The multistep IHC process involves fixation, embedding, sectioning, antigen retrieval, blocking, detection, counterstaining, mounting, and visualization, with interpretation relying on factors such as microanatomic distribution and staining intensity. Common errors in IHC such as non-specific staining, tissue artifacts, inadequately inactivation of endogenous peroxidase activity and cross-reactivity, can substantially affect the accuracy and reliability of results, thereby impacting the interpretation of biological findings. Serving diagnostic, prognostic, predictive, and therapeutic roles in various conditions, including tumors, infectious diseases, neurodegenerative disorders, and muscle diseases, IHC remains pivotal despite its intricate nature. The adoption of digital pathology emerges as a progressive enhancement, addressing limitations and ensuring more accurate analyses in histopathology.
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Affiliation(s)
- Dinku Yigzaw Mebratie
- University of Gondar, College of Veterinary Medicine and Animal Sciences, Department of Pathobiology, Ethiopia
| | - Gashaw Getaneh Dagnaw
- University of Gondar, College of Veterinary Medicine and Animal Sciences, Department of Biomedical Sciences, Ethiopia.
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Zhu J, Veeraraghavan H, Jiang J, Oh JH, Norton L, Deasy JO, Tannenbaum A. Wasserstein HOG: Local Directionality Extraction via Optimal Transport. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:916-927. [PMID: 37874704 PMCID: PMC11037420 DOI: 10.1109/tmi.2023.3325295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.
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Demircioğlu A. The effect of data resampling methods in radiomics. Sci Rep 2024; 14:2858. [PMID: 38310165 PMCID: PMC10838284 DOI: 10.1038/s41598-024-53491-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/05/2024] Open
Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
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Jia Y, Hou L, Zhao J, Ren J, Li D, Li H, Cui Y. Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study. Front Oncol 2024; 14:1333020. [PMID: 38347846 PMCID: PMC10860747 DOI: 10.3389/fonc.2024.1333020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Objective To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC). Methods A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models. Results The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility. Conclusion We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.
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Affiliation(s)
- Yaju Jia
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
- Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Lina Hou
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jintao Zhao
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2023. [PMID: 38146775 DOI: 10.1002/jmri.29161] [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: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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Li Q, Huang Y, Xia Y, Li M, Tang W, Zhang M, Zhao Z. Radiogenomics for predicting microsatellite instability status and PD-L1 expression with machine learning in endometrial cancers: A multicenter study. Heliyon 2023; 9:e23166. [PMID: 38149198 PMCID: PMC10750045 DOI: 10.1016/j.heliyon.2023.e23166] [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: 05/19/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023] Open
Abstract
Purpose To evaluate the effectiveness of machine learning model based on magnetic resonance imaging (MRI) in identifying microsatellite instability (MSI) status and PD-L1 expression in endometrial cancer (EC). Methods This retrospective study included 82 EC patients from 2 independent centers. Radiomics features from the intratumoral and peritumoral regions, obtained from four conventional MRI sequences (T2-weighted images; contrast-enhanced T1-weighted images; diffusion-weighted images; apparent diffusion coefficient), were combined with clinicopathologic characteristics to develop machine learning model for predicting MSI status and PD-L1 expression. 60 patients from center 1 were used as the training set for model construction, while 22 patients from center 2 were used as an external validation set for model evaluation. Results For predicting MSI status, the clinicopathologic model, radscore model, and combination model achieved area under the curves (AUCs) of 0.728, 0.833, and 0.889 in the training set, respectively, and 0.595, 0.790, and 0.848 in the validation set, respectively. For predicting PD-L1 expression, the clinicopathologic model, radscore model, and combination model achieved AUCs of 0.648, 0.814, and 0.834 in the training set, respectively, and 0.660, 0.708, and 0.764 in the validation set, respectively. Calibration curve analysis and decision curve analysis demonstrated good calibration and clinical utility of the combination model. Conclusion The machine learning model incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for predicting MSI status and PD-L1 expression in EC. This approach may contribute to precision medicine for EC patients.
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Affiliation(s)
- Qianling Li
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Zhejiang University School of Medicine, Shaoxing, 312000, China
| | - Ya'nan Huang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Yang Xia
- Department of Radiology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, 312000, China
| | - Meiping Li
- Department of Pathology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, 312000, China
| | - Wei Tang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, Hangzhou, 310000, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
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Yan B, Zhao T, Li Z, Ren J, Zhang Y. An MR-based radiomics nomogram including information from the peritumoral region to predict deep myometrial invasion in stage I endometrioid adenocarcinoma: a preliminary study. Br J Radiol 2023; 96:20230026. [PMID: 37751166 PMCID: PMC10607389 DOI: 10.1259/bjr.20230026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE To develop and validate an MR-based radiomics nomogram combining different imaging sequences (ADC mapping and T2 weighted imaging (T2WI)), different tumor regions (combined intra- and peritumoral regions), and different parameters (clinical features, tumor morphological features, and radiomics features) while considering different MR field strengths in predicting deep myometrial invasion (MI) in Stage I endometrioid adenocarcinoma (EEA). METHODS A total of 202 patients were retrospectively analyzed and divided into two cohorts (training cohort, 1.5 T MR, n = 131; validation cohort, 3.0 T MR, n = 71). Axial ADC mapping and T2WI were conducted. Radiomics features were extracted from intra- and peritumoral regions. Least absolute shrinkage and selection operator regression, univariate analysis, and multivariate logistic regression were used to select radiomics features and tumor morphological and clinical parameters. The area under the receiver operator characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and radiomics nomogram. RESULTS Ten radiomics features, 4 morphological parameters and 1 clinical characteristic were selected. The radiomics nomogram achieved good discrimination between the superficial and deep MI cohorts. The AUC was 0.927 (95% confidence interval [CI]: 0.865, 0.967) in the training cohort and 0.921 (95% CI: 0.872, 0.948) in the validation cohort. The specificity and sensitivity were 92.0 and 78.9% in the training cohort and 83.0 and 77.8% in the validation cohort, respectively. CONCLUSION The radiomics nomogram showed good performance in predicting the depth of MI in Stage I EEA before surgery and might be useful for surgical patient management. ADVANCES IN KNOWLEDGE An MR-based radiomics nomogram was useful for predicting deep MI in Stage I EEA patients (AUCtrain = 0.927, AUCvalidation = 0.921). The intra- and peritumoral radiomics features complemented each other. The nomogram was developed and validated with different MR field strengths, suggesting that the model demonstrates good generalizability.
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Affiliation(s)
| | - Tingting Zhao
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | | | | | - Yuchen Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
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11
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Guo W, Wang T, Lv B, Jiang J, Liu Y, Zhao P. Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review. J Cancer 2023; 14:3523-3531. [PMID: 38021155 PMCID: PMC10647186 DOI: 10.7150/jca.89347] [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: 08/21/2023] [Accepted: 10/08/2023] [Indexed: 12/01/2023] Open
Abstract
Endometrial cancer (EC) is a common gynecologic malignancy, with a rising trend in related mortality rates. The assessment based on imaging examinations contributes to the preoperative staging and surgical management of EC. However, conventional imaging diagnosis has limitations such as low accuracy and subjectivity. Radiomics, utilizing advanced feature analysis from medical images, extracts more information, ultimately establishing associations between imaging features and disease phenotypes. In recent years, radiomic studies on EC have emerged, employing radiomic features combined with clinical characteristics to model and predict histopathological features, protein expression, and clinical prognosis. This article elaborates on the application of radiomics in EC research and discusses its implications.
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Affiliation(s)
- Wenxiu Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
| | - Tong Wang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Binglin Lv
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Jie Jiang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Yao Liu
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Peng Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
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Coada CA, Santoro M, Zybin V, Di Stanislao M, Paolani G, Modolon C, Di Costanzo S, Genovesi L, Tesei M, De Leo A, Ravegnini G, De Biase D, Morganti AG, Lovato L, De Iaco P, Strigari L, Perrone AM. A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study. Cancers (Basel) 2023; 15:4534. [PMID: 37760503 PMCID: PMC10526953 DOI: 10.3390/cancers15184534] [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: 07/24/2023] [Revised: 08/23/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. METHODS Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). RESULTS In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. CONCLUSIONS Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.
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Affiliation(s)
- Camelia Alexandra Coada
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
| | - Miriam Santoro
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Vladislav Zybin
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Marco Di Stanislao
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
| | - Giulia Paolani
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Cecilia Modolon
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Stella Di Costanzo
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
| | - Lucia Genovesi
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
| | - Marco Tesei
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
| | - Antonio De Leo
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
- Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Gloria Ravegnini
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
| | - Dario De Biase
- Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
| | | | - Luigi Lovato
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Pierandrea De Iaco
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Anna Myriam Perrone
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (C.A.C.); (M.D.S.); (L.G.); (A.D.L.); (A.M.P.)
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (S.D.C.); (M.T.)
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Wang X, Liu Z, Yin X, Yang C, Zhang J. A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis. BMC Gastroenterol 2023; 23:308. [PMID: 37700238 PMCID: PMC10498531 DOI: 10.1186/s12876-023-02922-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
PURPOSE To study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery. METHODS The study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H). RESULTS The model including radiomic features and clinical features in the expanded data worked best in the validation group. CONCLUSION A logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China.
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China.
| | - Ziqi Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Bao Ding, 071000, China
| | - Chang Yang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Jushuo Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
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Piedimonte S, Rosa G, Gerstl B, Sopocado M, Coronel A, Lleno S, Vicus D. Evaluating the use of machine learning in endometrial cancer: a systematic review. Int J Gynecol Cancer 2023; 33:1383-1393. [PMID: 37666535 DOI: 10.1136/ijgc-2023-004622] [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] [Indexed: 09/06/2023] Open
Abstract
OBJECTIVE To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models. METHODS This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0. RESULTS Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%). CONCLUSION Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer. PROSPERO REGISTRATION NUMBER CRD42021269565.
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Affiliation(s)
- Sabrina Piedimonte
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
| | | | - Brigitte Gerstl
- The Rosa Institute, Sydney, New South Wales, Australia
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Mars Sopocado
- The Rosa Institute, Sydney, New South Wales, Australia
| | - Ana Coronel
- The Rosa Institute, Sydney, New South Wales, Australia
| | | | - Danielle Vicus
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
- Department of Gynecologic Oncology, Sunnybrook Health Sciences, Toronto, Ontario, Canada
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Yan B, Jia Y, Li Z, Ding C, Lu J, Liu J, Zhang Y. Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region. Acta Radiol 2023; 64:2636-2645. [PMID: 37312525 DOI: 10.1177/02841851231181681] [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] [Indexed: 06/15/2023]
Abstract
BACKGROUND Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. PURPOSE To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). MATERIAL AND METHODS A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. RESULTS Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. CONCLUSION The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.
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Affiliation(s)
- Bin Yan
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
- Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Yuxia Jia
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Zhihao Li
- GE Healthcare China, Xi'an, Shaanxi, PR China
| | - Caixia Ding
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jianrong Lu
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Yuchen Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Xi'an Jiaotong University, PR China
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16
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Li L, Wang M, Jiang X, Lin Y. Universal multi-factor feature selection method for radiomics-based brain tumor classification. Comput Biol Med 2023; 164:107122. [PMID: 37517322 DOI: 10.1016/j.compbiomed.2023.107122] [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: 02/08/2023] [Revised: 05/19/2023] [Accepted: 05/30/2023] [Indexed: 08/01/2023]
Abstract
Brain tumor mortality is high, and accurate classification before treatment can improve patient prognosis. Radiomics, which extracts numerous features from medical images, has been widely applied in brain tumor classification studies. Feature selection (FS) is a critical step in radiomics because it reduces redundant information and enhances classification performance. However, the lack of universal FS methods limits the development of radiomics-based brain tumor classification studies. To address this issue, we summarize the characteristics of the FS methods used in related studies and propose a universal method based on three selection factors called triple-factor cascaded selection (TFCS). Particularly, these factors correspond to the correlation between features and task labels, interdependence among features, and role of features in the model. The TFCS method divides FS into two steps. First, it utilizes mutual information to select features that are strongly correlated with the task and contain less redundant information. Recursive feature elimination is then employed to obtain the subset with the best classification performance. To validate the universality of the TFCS, we conducted experiments on seven datasets containing 13 brain tumor classification tasks and evaluated the overall performance using five types of indicators. Results: TFCS exhibited excellent overall performance for all tasks. Compared to the 13 related methods, it takes less time, has moderate parsimony, the best classification performance, adaptability, and stability, and shows better universality. Our study demonstrates that the reasonable utilization of multiple factors can enhance FS performance and provide new insights for future method design.
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Affiliation(s)
- Longfei Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China; Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China
| | - Meiyun Wang
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China; Department of Radiology, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoming Jiang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Post and Telecommunications, Chongqing, China.
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China; School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China; Hanwei IoT Institute, Zhengzhou University, Zhengzhou, China.
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17
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Wang W, Li X, Gao Y, Zheng H, Gao M. A nomogram prediction model for the TP53mut subtype in endometrial cancer based on preoperative noninvasive parameters. BMC Cancer 2023; 23:720. [PMID: 37528420 PMCID: PMC10394813 DOI: 10.1186/s12885-023-11234-1] [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: 12/23/2022] [Accepted: 07/27/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The molecular subtypes of endometrial carcinoma are significantly correlated with survival outcomes and can guide surgical methods and postoperative adjuvant therapy. Among them, the TP53mut subtype has the worst prognosis and can only be determined by detection after surgery. Therefore, identifying preoperative noninvasive clinical parameters for early prediction of the TP53mut subtype would provide important guidance in choosing the appropriate surgical method and early warning for clinicians. Our study aimed to establish a model for the early prediction of the TP53mut subtype by using preoperative noninvasive parameters of endometrial cancer and screen out potential TP53mut patients. METHODS Information and pathological specimens of 376 patients who underwent surgery for FIGO stage I-IV endometrial cancer in the Department of Gynecology, Peking University Cancer Hospital, from June 2011 to July 2020 were collected, and 178 cases were finally included in the study as the training dataset (part A). Thirty-six cases from January 2022 to March 2023 were collected as the validation dataset (part B). Molecular subtyping was performed using a one-stop next-generation sequencing (NGS) approach. Compared with the TP53mut subtype, the POLE EDM, MSI-H and TP53 wild-type subtypes were defined as non-TP53mut subtypes. Univariate Cox regression analysis and multivariate logistic analysis were performed to determine the preoperative clinical parameters associated with the TP53mut subtype. A nomogram prediction model was established using preoperative noninvasive parameters, and its efficacy in predicting TP53mut subtype and survival outcomes was verified. RESULTS The TP53mut subtype was identified in 12.4% of the part A and 13.9% of the part B. Multivariate logistic regression analysis showed that HDL-C/LDL-C level, CA125 level, and cervical or lower uterine involvement were independent influencing factors associated with the TP53mut subtype (p = 0.016, 0.047, <0.001). A TP53mut prognostic model (TPMM) was constructed based on the factors identified in the multivariate analysis, namely, TPMM = -1.385 × HDL-C/LDL-C + 1.068 × CA125 + 1.89 × CI or LUI, with an AUC = 0.768 (95% CI, 0.642 to 0.893) in the part A. The AUC of TPMM for predicting TP53mut subtype in the part B was 0.781(95% CI, 0.581 to 0.980). The progression-free survival (PFS) and overall survival (OS) of patients with the TP53mut subtype were significantly worse than those of patients with the non-TP53mut subtype, as predicted by the model in the part A. CONCLUSIONS TP53mut prediction model (TPMM) had good diagnostic accuracy, and survival analysis showed the model can identify patients with different prognostic risk.
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Affiliation(s)
- Wei Wang
- Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, 100142, China
| | - Xiaoting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, 100142, China
| | - Yunong Gao
- Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, 100142, China
| | - Hong Zheng
- Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, 100142, China
| | - Min Gao
- Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, 100142, China.
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Hoivik EA. Using an MRI-based radiomics model to predict recurrence of endometrial cancer: a step towards meeting a key clinical need. Eur Radiol 2023; 33:5812-5813. [PMID: 37311806 DOI: 10.1007/s00330-023-09764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 03/18/2023] [Accepted: 04/11/2023] [Indexed: 06/15/2023]
Affiliation(s)
- Erling A Hoivik
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Department of Pathology, Haukeland University Hospital, Bergen, Norway.
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19
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Tan Q, Wang Q, Jin S, Zhou F, Zou X. Network Evolution Model-based prediction of tumor mutation burden from radiomic-clinical features in endometrial cancers. BMC Cancer 2023; 23:712. [PMID: 37525139 PMCID: PMC10388464 DOI: 10.1186/s12885-023-11118-4] [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: 03/13/2023] [Accepted: 06/27/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Endometrial Cancer (EC) is one of the most prevalent malignancies that affect the female population globally. In the context of immunotherapy, Tumor Mutation Burden (TMB) in the DNA polymerase epsilon (POLE) subtype of this cancer holds promise as a viable therapeutic target. METHODS We devised a method known as NEM-TIE to forecast the TMB status of patients with endometrial cancer. This approach utilized a combination of the Network Evolution Model, Transfer Information Entropy, Clique Percolation (CP) methodology, and Support Vector Machine (SVM) classification. To construct the Network Evolution Model, we employed an adjacency matrix that utilized transfer information entropy to assess the information gain between nodes of radiomic-clinical features. Subsequently, using the CP algorithm, we unearthed potentially pivotal modules in the Network Evolution Model. Finally, the SVM classifier extracted essential features from the module set. RESULTS Upon analyzing the importance of modules, we discovered that the dependence count energy in tumor volumes-of-interest holds immense significance in distinguishing TMB statuses among patients with endometrial cancer. Using the 13 radiomic-clinical features extracted via NEM-TIE, we demonstrated that the area under the receiver operating characteristic curve (AUROC) in the test set is 0.98 (95% confidence interval: 0.95-1.00), surpassing the performance of existing techniques such as the mRMR and Laplacian methods. CONCLUSIONS Our study proposed the NEM-TIE method as a means to identify the TMB status of patients with endometrial cancer. The integration of radiomic-clinical data utilizing the NEM-TIE method may offer a novel technology for supplementary diagnosis.
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Affiliation(s)
- Qing Tan
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China
| | - Qian Wang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, 430072, China
| | - Suoqin Jin
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, 430072, China.
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China.
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20
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Kodada D, Hyblova M, Krumpolec P, Janostiakova N, Barath P, Grendar M, Blandova G, Petrovic O, Janega P, Repiska V, Minarik G. The Potential of Liquid Biopsy in Detection of Endometrial Cancer Biomarkers: A Pilot Study. Int J Mol Sci 2023; 24:ijms24097811. [PMID: 37175518 PMCID: PMC10178554 DOI: 10.3390/ijms24097811] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Endometrial cancer belongs to the most common gynecologic cancer types globally, with increasing incidence. There are numerous ways of classifying different cases. The most recent decade has brought advances in molecular classification, which show more accurate prognostic factors and the possibility of personalised adjuvant treatment. In addition, diagnostic approaches lag behind these advances, with methods causing patients discomfort while lacking the reproducibility of tissue sampling for biopsy. Minimally invasive liquid biopsies could therefore represent an alternative screening and diagnostic approach in patients with endometrial cancer. The method could potentially detect molecular changes in this cancer type and identify patients at early stages. In this pilot study, we tested such a detection method based on circulating tumour DNA isolated from the peripheral blood plasma of 21 Slovak endometrial cancer patients. We successfully detected oncomutations in the circulating DNA of every single patient, although the prognostic value of the detected mutations failed to offer certainty. Furthermore, we detected changes associated with clonal hematopoiesis, including DNMT3A mutations, which were present in the majority of circulating tumour DNA samples.
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Affiliation(s)
- Dominik Kodada
- Medirex Group Academy, 94905 Nitra, Slovakia
- Faculty of Medicine, Comenius University in Bratislava, 84215 Bratislava, Slovakia
| | | | | | - Nikola Janostiakova
- Medirex Group Academy, 94905 Nitra, Slovakia
- Faculty of Medicine, Comenius University in Bratislava, 84215 Bratislava, Slovakia
| | | | - Marian Grendar
- Medirex Group Academy, 94905 Nitra, Slovakia
- Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia
| | - Gabriela Blandova
- Faculty of Medicine, Comenius University in Bratislava, 84215 Bratislava, Slovakia
| | | | - Pavol Janega
- Medirex Group Academy, 94905 Nitra, Slovakia
- Faculty of Medicine, Comenius University in Bratislava, 84215 Bratislava, Slovakia
| | - Vanda Repiska
- Faculty of Medicine, Comenius University in Bratislava, 84215 Bratislava, Slovakia
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Di Donato V, Kontopantelis E, Cuccu I, Sgamba L, Golia D'Augè T, Pernazza A, Della Rocca C, Manganaro L, Catalano C, Perniola G, Palaia I, Tomao F, Giannini A, Muzii L, Bogani G. Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis. Int J Gynecol Cancer 2023:ijgc-2023-004313. [PMID: 37094971 DOI: 10.1136/ijgc-2023-004313] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Endometrial carcinoma is the most common gynecological tumor in developed countries. Clinicopathological factors and molecular subtypes are used to stratify the risk of recurrence and to tailor adjuvant treatment. The present study aimed to assess the role of radiomics analysis in pre-operatively predicting molecular or clinicopathological prognostic factors in patients with endometrial carcinoma. METHODS Literature was searched for publications reporting radiomics analysis in assessing diagnostic performance of MRI for different outcomes. Diagnostic accuracy performance of risk prediction models was pooled using the metandi command in Stata. RESULTS A search of MEDLINE (PubMed) resulted in 153 relevant articles. Fifteen articles met the inclusion criteria, for a total of 3608 patients. MRI showed pooled sensitivity and specificity 0.785 and 0.814, respectively, in predicting high-grade endometrial carcinoma, deep myometrial invasion (pooled sensitivity and specificity 0.743 and 0.816, respectively), lymphovascular space invasion (pooled sensitivity and specificity 0.656 and 0.753, respectively), and nodal metastasis (pooled sensitivity and specificity 0.831 and 0.736, respectively). CONCLUSIONS Pre-operative MRI-radiomics analyses in patients with endometrial carcinoma is a good predictor of tumor grading, deep myometrial invasion, lymphovascular space invasion, and nodal metastasis.
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Affiliation(s)
- Violante Di Donato
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Ilaria Cuccu
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovica Sgamba
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Tullio Golia D'Augè
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Carlo Della Rocca
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgia Perniola
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Innocenza Palaia
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Federica Tomao
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Andrea Giannini
- Department of Medical and Surgical Sciences and Translational Medicine, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovico Muzii
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, IRCCS National Cancer Institute, Milan, Italy
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22
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Song XL, Luo HJ, Ren JL, Yin P, Liu Y, Niu J, Hong N. Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer. LA RADIOLOGIA MEDICA 2023; 128:242-251. [PMID: 36656410 DOI: 10.1007/s11547-023-01590-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/04/2023] [Indexed: 01/20/2023]
Abstract
PURPOSE To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC). MATERIALS AND METHODS This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models. RESULTS Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581-0.783. CONCLUSION The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.
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Affiliation(s)
- Xiao-Li Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.,Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Hong-Jian Luo
- Department of Radiology, Peking University People's Hospital, Beijing, China.,Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University, The First People's Hospital of Zunyi, Zunyi, Guizhou Province, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnosics, GE Healthcare, Beijing, China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jinliang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China.
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23
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Development of MRI-based radiomics predictive model for classifying endometrial lesions. Sci Rep 2023; 13:1590. [PMID: 36709399 PMCID: PMC9884294 DOI: 10.1038/s41598-023-28819-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 01/25/2023] [Indexed: 01/30/2023] Open
Abstract
An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.
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24
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Ghaffari Laleh N, Ligero M, Perez-Lopez R, Kather JN. Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clin Cancer Res 2023; 29:316-323. [PMID: 36083132 DOI: 10.1158/1078-0432.ccr-22-0390] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/26/2022] [Accepted: 08/29/2022] [Indexed: 01/19/2023]
Abstract
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.
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Affiliation(s)
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
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25
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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26
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Lin Z, Wang T, Li H, Xiao M, Ma X, Gu Y, Qiang J. Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer. Quant Imaging Med Surg 2023; 13:108-120. [PMID: 36620141 PMCID: PMC9816750 DOI: 10.21037/qims-22-255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022]
Abstract
Background Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC. Methods A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA). Results Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model. Conclusions The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.
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Affiliation(s)
- Zijing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China;,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xiaoliang Ma
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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27
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Hinzpeter R, Kulanthaivelu R, Kohan A, Avery L, Pham NA, Ortega C, Metser U, Haider M, Veit-Haibach P. CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling. Cancers (Basel) 2022; 14:cancers14246224. [PMID: 36551709 PMCID: PMC9776865 DOI: 10.3390/cancers14246224] [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: 10/26/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42-86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution's web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.
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Affiliation(s)
- Ricarda Hinzpeter
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Correspondence: ; Tel.: +1-416-340-4800
| | - Roshini Kulanthaivelu
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Andres Kohan
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Lisa Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Nhu-An Pham
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Masoom Haider
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
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28
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Lu W, Qiu Y, Wu Y, Li J, Chen R, Chen S, Lin Y, OuYang L, Chen J, Chen F, Qiu S. RADIOMICS BASED ON TWO-DIMENSIONAL AND THREE-DIMENSIONAL ULTRASOUND FOR EXTRATHYROIDAL EXTENSION FEATURE PREDICTION IN PAPILLARY THYROID CARCINOMA. ACTA ENDOCRINOLOGICA (BUCHAREST, ROMANIA : 2005) 2022; 18:407-416. [PMID: 37152886 PMCID: PMC10162833 DOI: 10.4183/aeb.2022.407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Aim To evaluate the diagnostic performance of radiomics features of two-dimensional (2D) and three-dimensional (3D) ultrasound (US) in predicting extrathyroidal extension (ETE) status in papillary thyroid carcinoma (PTC). Patients and Methods 2D and 3D thyroid ultrasound images of 72 PTC patients confirmed by pathology were retrospectively analyzed. The patients were assigned to ETE and non-ETE. The regions of interest (ROIs) were obtained manually. From these images, a larger number of radiomic features were automatically extracted. Lastly, the diagnostic abilities of the radiomics models and a radiologist were evaluated using receiver operating characteristic (ROC) analysis. We extracted 1693 texture features firstly. Results The area under the ROC curve (AUC) of the radiologist was 0.65. For 2D US, the mean AUC of the three classifiers separately were: 0.744 for logistic regression (LR), 0.694 for multilayer perceptron (MLP), 0.733 for support vector machines (SVM). For 3D US they were 0.876 for LR, 0.825 for MLP, 0.867 for SVM. The diagnostic efficiency of the radiomics was better than radiologist. The LR model had favorable discriminate performance with higher area under the curve. Conclusion Radiomics based on US image had the potential to preoperatively predict ETE. Radiomics based on 3D US images presented more advantages over radiomics based on 2D US images and radiologist.
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Affiliation(s)
- W.J. Lu
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - Y.R. Qiu
- The Second Clinical School of Guangzhou Medical University − Department of Clinical Medicine, Guangzhou, Guangdong, China
| | - Y.W. Wu
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - J. Li
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - R. Chen
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - S.N. Chen
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - Y.Y. Lin
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - L.Y. OuYang
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - J.Y. Chen
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - F. Chen
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
| | - S.D. Qiu
- The Second Affiliated Hospital of Guangzhou Medical University − Ultrasound
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29
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Wang H, Xu Z, Zhang H, Huang J, Peng H, Zhang Y, Liang C, Zhao K, Liu Z. The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer. Quant Imaging Med Surg 2022; 12:4402-4413. [PMID: 36060586 PMCID: PMC9403574 DOI: 10.21037/qims-22-77] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery. METHODS The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ2 test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables. RESULTS There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601). CONCLUSIONS It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.
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Affiliation(s)
- Huihui Wang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
- Shantou University Medical College, Shantou, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Haochen Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Jia Huang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Haien Peng
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 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
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30
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Bhardwaj V, Sharma A, Parambath SV, Gul I, Zhang X, Lobie PE, Qin P, Pandey V. Machine Learning for Endometrial Cancer Prediction and Prognostication. Front Oncol 2022; 12:852746. [PMID: 35965548 PMCID: PMC9365068 DOI: 10.3389/fonc.2022.852746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
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Affiliation(s)
- Vipul Bhardwaj
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Arundhiti Sharma
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | | | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Xi Zhang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peter E. Lobie
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peiwu Qin
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Vijay Pandey
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Vijay Pandey,
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Wang Q, Zhang Y, Zhang E, Xing X, Chen Y, Nie K, Yuan H, Su MY, Lang N. A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone. Front Oncol 2022; 12:894696. [PMID: 35800059 PMCID: PMC9253421 DOI: 10.3389/fonc.2022.894696] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.Material and MethodsA retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.ResultsThe immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p < 0.001. For p53, SINS (p = 0.030) and Enneking stage (p = 0.017) were higher in mutant than wild-type groups. The VEGF radiomics model built using 3 features achieved an area under the curve (AUC) of 0.88, and the p53 radiomics model built using 4 features had an AUC of 0.79. The conventional model built using SINS, and the Enneking stage had a slightly lower AUC of 0.81 for VEGF and 0.72 for p53.Conclusionp53 and VEGF are associated with prognosis in patients with spinal GCTB, and the radiomics analysis based on preoperative CT provides a feasible method for the evaluation of these two biomarkers, which may aid in choosing better management strategies.
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Affiliation(s)
- Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yang Zhang
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, United States
- Department of Radiation Oncology, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Enlong Zhang
- Department of Radiology, Peking University International Hospital, Beijing, China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Ke Nie
- Department of Radiation Oncology, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, United States
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- *Correspondence: Ning Lang, ; Min-Ying Su,
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Ning Lang, ; Min-Ying Su,
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Affiliation(s)
| | - Alexander T Pearson
- Biological Sciences Division, University of Chicago, Chicago, IL, USA.
- University of Chicago Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
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Degasperi A, Zou X, Amarante TD, Martinez-Martinez A, Koh GCC, Dias JML, Heskin L, Chmelova L, Rinaldi G, Wang VYW, Nanda AS, Bernstein A, Momen SE, Young J, Perez-Gil D, Memari Y, Badja C, Shooter S, Czarnecki J, Brown MA, Davies HR, Nik-Zainal S. Substitution mutational signatures in whole-genome-sequenced cancers in the UK population. Science 2022; 376:science.abl9283. [PMID: 35949260 PMCID: PMC7613262 DOI: 10.1126/science.abl9283] [Citation(s) in RCA: 110] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage and repair processes that have arisen in each patient's cancer. We performed mutational signature analyses on 12,222 WGS tumor-normal matched pairs, from patients recruited via the UK National Health Service. We contrasted our results to two independent cancer WGS datasets, the International Cancer Genome Consortium (ICGC) and Hartwig Foundation, involving 18,640 WGS cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. Critically, we show for each organ, that cancers have a limited number of 'common' signatures and a long tail of 'rare' signatures. We provide a practical solution for utilizing this concept of common versus rare signatures in future analyses.
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Affiliation(s)
- Andrea Degasperi
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Xueqing Zou
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Tauanne Dias Amarante
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Andrea Martinez-Martinez
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Gene Ching Chiek Koh
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - João M. L. Dias
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Laura Heskin
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Lucia Chmelova
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Giuseppe Rinaldi
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Valerie Ya Wen Wang
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Arjun S. Nanda
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Aaron Bernstein
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Sophie E. Momen
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Jamie Young
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Daniel Perez-Gil
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Yasin Memari
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Cherif Badja
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Scott Shooter
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Jan Czarnecki
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Matthew A. Brown
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London, EC1M 6BQ, UK
- Faculty of Life Sciences and Medicine, King’s College London, London SE19RT, UK
| | - Helen R. Davies
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Serena Nik-Zainal
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge CB2 9NB, UK
- MRC Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, UK
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Njoku K, Barr CE, Crosbie EJ. Current and Emerging Prognostic Biomarkers in Endometrial Cancer. Front Oncol 2022; 12:890908. [PMID: 35530346 PMCID: PMC9072738 DOI: 10.3389/fonc.2022.890908] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/28/2022] [Indexed: 12/19/2022] Open
Abstract
Endometrial cancer is the most common gynaecological malignancy in high income countries and its incidence is rising. Whilst most women with endometrial cancer are diagnosed with highly curable disease and have good outcomes, a significant minority present with adverse clinico-pathological characteristics that herald a poor prognosis. Prognostic biomarkers that reliably select those at greatest risk of disease recurrence and death can guide management strategies to ensure that patients receive appropriate evidence-based and personalised care. The Cancer Genome Atlas substantially advanced our understanding of the molecular diversity of endometrial cancer and informed the development of simplified, pragmatic and cost-effective classifiers with prognostic implications and potential for clinical translation. Several blood-based biomarkers including proteins, metabolites, circulating tumour cells, circulating tumour DNA and inflammatory parameters have also shown promise for endometrial cancer risk assessment. This review provides an update on the established and emerging prognostic biomarkers in endometrial cancer.
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Affiliation(s)
- Kelechi Njoku
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Stoller Biomarker Discovery Centre, University of Manchester, Manchester, United Kingdom
- Department of Obstetrics and Gynaecology, St Mary’s Hospital, Manchester, University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Chloe E. Barr
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Obstetrics and Gynaecology, St Mary’s Hospital, Manchester, University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Emma J. Crosbie
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Obstetrics and Gynaecology, St Mary’s Hospital, Manchester, University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
- *Correspondence: Emma J. Crosbie,
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Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. RESULTS Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35-0.84). The features selected by different models varied largely (0.19; range 0.02-0.42), although the selected features themselves were highly correlated (0.71; range 0.4-0.92). CONCLUSIONS Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
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36
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Hoivik EA, Hodneland E, Dybvik JA, Wagner-Larsen KS, Fasmer KE, Berg HF, Halle MK, Haldorsen IS, Krakstad C. A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol 2021; 4:1363. [PMID: 34873276 PMCID: PMC8648740 DOI: 10.1038/s42003-021-02894-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022] Open
Abstract
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.
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Affiliation(s)
- Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Julie A Dybvik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Mari K Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
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Endometrial cancer from early to advanced-stage disease: an update for radiologists. Abdom Radiol (NY) 2021; 46:5325-5336. [PMID: 34297164 DOI: 10.1007/s00261-021-03220-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 01/23/2023]
Abstract
The purpose of this article is to review the current molecular classification of endometrial cancer, the imaging findings in early and advanced disease, and the current management strategies, focusing on the new systemic therapies for advanced EC. In recent years, the management of endometrial cancer has significantly changed. The molecular characterization of endometrial cancer has shed new light into the biologic behavior of this disease, the International Federation of Gynecology and Obstetrics staging system was recently revised, and imaging was formally incorporated in the management of endometrial cancer. Recent genomic analysis of endometrial cancer led to the approval of new molecular-targeted therapies and immune checkpoint inhibitors. Imaging allows assessment of myometrial invasion, cervical stromal extension, lymph node involvement and distant metastases, and has a crucial role for treatment planning. Treatment strategies, which include surgery, radiation and systemic therapies are based on accurate staging and risk stratification.
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Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021; 94:20201314. [PMID: 34233456 PMCID: PMC9327743 DOI: 10.1259/bjr.20201314] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.
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Affiliation(s)
- Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Gabriele Maria Nicolino
- Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Federica Martorana
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland.,Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland
| | - Ilaria Colombo
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Stefania Rizzo
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland.,Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, Lugano (CH), Switzerland
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39
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Hope A, Verduin M, Dilling TJ, Choudhury A, Fijten R, Wee L, Aerts HJWL, El Naqa I, Mitchell R, Vooijs M, Dekker A, de Ruysscher D, Traverso A. Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers. Cancers (Basel) 2021; 13:2382. [PMID: 34069307 PMCID: PMC8156328 DOI: 10.3390/cancers13102382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/21/2021] [Accepted: 05/03/2021] [Indexed: 11/16/2022] Open
Abstract
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
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Affiliation(s)
- Andrew Hope
- Department of Radiation Oncology, University of Toronto, Toronto, ON 5MT 1P5, Canada;
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON 5MT 1P5, Canada
| | - Maikel Verduin
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Thomas J Dilling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Ananya Choudhury
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Leonard Wee
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Hugo JWL Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA;
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, 6228 ET Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Ross Mitchell
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Marc Vooijs
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Andre Dekker
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
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