51
|
Jiang X, Jia H, Zhang Z, Wei C, Wang C, Dong J. The Feasibility of Combining ADC Value With Texture Analysis of T 2WI, DWI and CE-T 1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma. Front Oncol 2022; 11:805545. [PMID: 35127515 PMCID: PMC8811460 DOI: 10.3389/fonc.2021.805545] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/29/2021] [Indexed: 01/13/2023] Open
Abstract
PURPOSE To evaluate the feasibility of apparent diffusion coefficient (ADC) value combined with texture analysis (TA) in preoperatively predicting the expression levels of Ki-67 and p53 in endometrial carcinoma (EC) patients. METHODS Clinical, pathological and MRI findings of 110 EC patients were analyzed retrospectively. The expression levels of Ki-67 and p53 in EC tissues were detected by immunohistochemistry. ADC value was calculated, and three-dimensional (3D) texture features were measured on T2-weighted images (T2WI), diffusion-weighted images (DWI), and contrast-enhanced T1-weighted images (CE-T1WI). The univariate and multivariate logistic regression and cross-validations were used for the selection of texture features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of prediction model by the area under the curve (AUC) in the training and validation cohorts. RESULTS Significant differences of the ADC values were found in predicting Ki-67 and p53 (P=0.039, P=0.007). The AUC of the ADC value in predicting the expression levels of Ki-67 and p53 were 0.698, 0.853 and 0.626, 0.702 in the training and validation cohorts. The AUC of the TA model based on T2WI, DWI, CE-T1WI, and ADC value combined with T2WI + DWI + CE-T1WI in the training and validation cohorts for predicting the expression of Ki-67 were 0.741, 0.765, 0.733, 0.922 and 0.688, 0.691, 0.651, 0.938, respectively, and for predicting the expression of p53 were 0.763, 0.805, 0.781, 0.901 and 0.796, 0.713, 0.657, 0.922, respectively. CONCLUSION ADC values combined with TA are beneficial for predicting the expression levels of Ki-67 and p53 in EC patients before surgery, and they provide higher auxiliary diagnostic values for clinical application.
Collapse
Affiliation(s)
- Xueyan Jiang
- Department of Radiology, Bengbu Medical College, Bengbu, China
| | - Haodong Jia
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Zhongyuan Zhang
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Chao Wei
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Chuanbin Wang
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Jiangning Dong
- Department of Radiology, Bengbu Medical College, Bengbu, China.,Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| |
Collapse
|
52
|
Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
Collapse
|
53
|
Abstract
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
Collapse
Affiliation(s)
| | - Ihab R Kamel
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X Bai
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
54
|
Wu M, Zhang Y, Zhang J, Zhang Y, Wang Y, Chen F, Luo Y, He S, Liu Y, Yang Q, Li Y, Wei H, Zhang H, Lu N, Wang S, Guo Y, Ye Z, Liu Y. A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study. Front Oncol 2022; 11:688679. [PMID: 35083133 PMCID: PMC8784873 DOI: 10.3389/fonc.2021.688679] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 12/16/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Based on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC). METHODS 131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models' predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models. RESULTS For TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79). CONCLUSION Compared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.
Collapse
Affiliation(s)
- Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yanyan Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jianing Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yina Wang
- Department of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuai He
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanying Li
- Department of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhang
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Nian Lu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China
| | - Sicong Wang
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Yan Guo
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| |
Collapse
|
55
|
Barat M, Cottereau AS, Gaujoux S, Tenenbaum F, Sibony M, Bertherat J, Libé R, Gaillard M, Jouinot A, Assié G, Hoeffel C, Soyer P, Dohan A. Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers (Basel) 2022; 14:cancers14030569. [PMID: 35158836 PMCID: PMC8833697 DOI: 10.3390/cancers14030569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Non-invasive characterization of adrenal lesions requires a rigorous approach. Although CT is the cornerstone of adrenal lesion characterization, a multimodality multiparametric imaging approach helps improve confidence in adrenal lesion characterization. Abstract Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. 18F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.
Collapse
Affiliation(s)
- Maxime Barat
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anne-Ségolène Cottereau
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Sébastien Gaujoux
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pancreatic and Endocrine Surgery, Pitié-Salpetrière Hospital, AP-HP, 75013 Paris, France
| | - Florence Tenenbaum
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Mathilde Sibony
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pathology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Jérôme Bertherat
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Rossella Libé
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Martin Gaillard
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Digestive, Hepatobiliary and Endocrine Surgery, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Anne Jouinot
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Guillaume Assié
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | | | - Philippe Soyer
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anthony Dohan
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Correspondence:
| |
Collapse
|
56
|
Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA, Thaiss WM, Bloehdorn J, Beer AJ, Stilgenbauer S, Beer M, Götz M. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers (Basel) 2022; 14:393. [PMID: 35053554 PMCID: PMC8773890 DOI: 10.3390/cancers14020393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
Collapse
Affiliation(s)
- Catharina Silvia Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christoph Gerhard Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Sherin Achilles
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Marc Fabian Mezger
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Johannes Bloehdorn
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ambros J Beer
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stephan Stilgenbauer
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- German Cancer Research Center (DKFZ), Division Medical Image Computing, 69120 Heidelberg, Germany
| |
Collapse
|
57
|
Urraro F, Nardone V, Reginelli A, Varelli C, Angrisani A, Patanè V, D'Ambrosio L, Roccatagliata P, Russo GM, Gallo L, De Chiara M, Altucci L, Cappabianca S. MRI Radiomics in Prostate Cancer: A Reliability Study. Front Oncol 2022; 11:805137. [PMID: 34993153 PMCID: PMC8725993 DOI: 10.3389/fonc.2021.805137] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols, as well as by the segmentation of the region of interest (ROI). Prostate cancer (PCa) represents a difficult playground for this technique, due to discrepancies in the identification of the cancer lesion and the heterogeneity of the acquisition protocols. The aim of this study was to investigate the reliability of radiomics in PCa magnetic resonance imaging (MRI). METHODS A homogeneous cohort of patients with a PSA rise that underwent multiparametric MRI imaging of the prostate before biopsy was tested in this study. All the patients were acquired with the same MRI scanner, with a standardized protocol. The identification and the contouring of the region of interest (ROI) of an MRI suspicious cancer lesion were done by two radiologists with great experience in prostate cancer (>10 years). After the segmentation, the texture features were extracted with LIFEx. Texture features were then tested with intraclass coefficient correlation (ICC) analysis to analyze the reliability of the segmentation. RESULTS Forty-four consecutive patients were included in the present analysis. In 26 patients (59.1%), the prostate biopsy confirmed the presence of prostate cancer, which was scored as Gleason 6 in 6 patients (13.6%), Gleason 3 + 4 in 8 patients (18.2%), and Gleason 4 + 3 in 12 patients (27.3%). The reliability analysis conversely showed poor reliability in the majority of the MRI acquisition (61% in T2, 89% in DWI50, 44% in DWI400, and 83% in DWI1,500), with ADC acquisition only showing better reliability (poor reliability in only 33% of the texture features). CONCLUSIONS The low ratio of reliability in a monoinstitutional homogeneous cohort represents a significant alarm bell for the application of MRI radiomics in the field of prostate cancer. More work is needed in a clinical setting to further study the potential of MRI radiomics in prostate cancer.
Collapse
Affiliation(s)
- Fabrizio Urraro
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | | | - Antonio Angrisani
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Vittorio Patanè
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Luca D'Ambrosio
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Pietro Roccatagliata
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Luigi Gallo
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| |
Collapse
|
58
|
Kim M, Jee WH, Lee Y, Hong JH, Jung CK, Chung YG, Lee SY. Tumor Margin Infiltration in Soft Tissue Sarcomas: Prediction Using 3T MRI Texture Analysis. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:112-126. [PMID: 36237350 PMCID: PMC9238208 DOI: 10.3348/jksr.2021.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 05/11/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Minji Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Radiology, Uijeongbu St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won-Hee Jee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Youngjun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Radiology, Uijeongbu St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Ji Hyun Hong
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Chan Kwon Jung
- Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yang-Guk Chung
- Departments of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
59
|
Nowakowski A, Lahijanian Z, Panet-Raymond V, Siegel PM, Petrecca K, Maleki F, Dankner M. Radiomics as an emerging tool in the management of brain metastases. Neurooncol Adv 2022; 4:vdac141. [PMID: 36284932 PMCID: PMC9583687 DOI: 10.1093/noajnl/vdac141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.
Collapse
Affiliation(s)
- Alexander Nowakowski
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Zubin Lahijanian
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Valerie Panet-Raymond
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Peter M Siegel
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Québec, Canada
| | - Farhad Maleki
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Matthew Dankner
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| |
Collapse
|
60
|
Park HJ, Qin L, Bakouny Z, Krajewski KM, Van Allen EM, Choueiri TK, Shinagare AB. OUP accepted manuscript. Oncologist 2022; 27:389-397. [PMID: 35348767 PMCID: PMC9074990 DOI: 10.1093/oncolo/oyac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 01/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background Materials and Methods Results Conclusion
Collapse
Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziad Bakouny
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Katherine M Krajewski
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Toni K Choueiri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Atul B Shinagare
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding author: Atul B. Shinagare, Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Tel.: +1 6176322988; Fax: +1 6175828574;
| |
Collapse
|
61
|
Kim KE, Kim CK. Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer. Abdom Radiol (NY) 2022; 47:352-361. [PMID: 34605967 DOI: 10.1007/s00261-021-03288-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 09/19/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer. METHODS This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2-6 mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS). RESULTS During a median follow-up of 36 months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3-5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p < 0.05). Regarding the 2-year RFS for CE-T1 MPP and T2 kurtosis at SSF5, and CE-T1 MPP at SSF6, patients with > optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05). CONCLUSION Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.
Collapse
Affiliation(s)
- Ka Eun Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
| |
Collapse
|
62
|
Li W, Xu C, Ye Z. Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR. Front Oncol 2021; 11:758062. [PMID: 34868970 PMCID: PMC8637752 DOI: 10.3389/fonc.2021.758062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Background Pancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images. Materials and Methods Totally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model. Results No significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433-0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695. Conclusions The maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.
Collapse
Affiliation(s)
- Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chao Xu
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| |
Collapse
|
63
|
Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [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: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
Collapse
|
64
|
Papoutsaki MV, Sidhu HS, Dikaios N, Singh S, Atkinson D, Kanber B, Beale T, Morley S, Forster M, Carnell D, Mendes R, Punwani S. Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers. NMR IN BIOMEDICINE 2021; 34:e4587. [PMID: 34240782 DOI: 10.1002/nbm.4587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.
Collapse
Affiliation(s)
| | | | - Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Baris Kanber
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Timothy Beale
- Department of Radiology, University College London Hospital, London, UK
| | - Simon Morley
- Department of Radiology, University College London Hospital, London, UK
| | - Martin Forster
- Department of Oncology, University College London, Cancer Institute, London, UK
- Department of Oncology, University College London Hospital, London, UK
| | - Dawn Carnell
- Department of Oncology, University College London Hospital, London, UK
| | - Ruheena Mendes
- Department of Oncology, University College London Hospital, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| |
Collapse
|
65
|
Erkoc M, Bozkurt M, Besiroglu H, Canat L, Atalay HA. Success of extracorporeal shock wave lithotripsy based on CT texture analysis. Int J Clin Pract 2021; 75:e14823. [PMID: 34491588 DOI: 10.1111/ijcp.14823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/28/2021] [Accepted: 09/06/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE The aims of the study were to evaluate whether computerised tomography texture analysis (CTTA) based on non-contrast computed tomography (NCCT) has predictive value for the success of extracorporeal-shockwave lithotripsy (ESWL) in upper urinary tract stones (UUTS). METHODS This study included 156 of 356 patients undergoing ESWL for UUTS sized 0.5-2 cm from 2015 to 2019. Patients with congenital kidney anomalies, radiolucent stones, multiple stones, treated for upper urinary tract stones previously and lower pole stones were excluded from study. The number of ESWL sessions of the patients was as follows: 78 (50%) patients had 1 session, 30 (19.2%) patients had 2 sessions and 48 (30.8%) patients had >2 sessions. First- and second-order CTTA properties of patients' UUTS were evaluated using texture analysis software (LIFEx Software). Other clinical features such as Hounsfield Unit (HU), initial stone size, body-mass index (BMI) and skin to stone distance (SSD) was recorded. The patients were divided into two groups according to ESWL success. Cases with residual stones larger than 4 mm were considered failed cases. RESULTS BMI, the standard deviation of HU, SSD, skewness, kurtosis, entropy and all second-order statistics were found to be statistically different between the two groups except for correlation (P < .05). Multivariate analysis showed longer SSD and four new parameters of CTTA (kurtosis, entropy, dissimilarity and energy by the distribution of pixel grey levels in the UUTS) to be significant predictors for unsuccessful ESWL outcomes. SSD and second-order CTTA properties (dissimilarity and energy) had an area under ROC curve of 0.802, 0.850 and 0.824 at a 95% confidence interval. ESWL success rate in all patients was 76.9%. CONCLUSION CTTA can help select patients who will undergo ESWL for upper urinary tract stones. Thus, we can reduce treatment costs and ESWL complications by preventing unnecessary ESWL applications.
Collapse
Affiliation(s)
- Mustafa Erkoc
- Department of Urology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Muammer Bozkurt
- Department of Urology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Huseyin Besiroglu
- Department of Urology, Faculty of Medical School, Istanbul-Cerrahpasa University, Istanbul, Turkey
| | - Lutfi Canat
- Department of Urology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Hasan A Atalay
- Department of Urology, Beylikduzu State Hospital, Istanbul, Turkey
| |
Collapse
|
66
|
Bonnin A, Durot C, Barat M, Djelouah M, Grange F, Mulé S, Soyer P, Hoeffel C. CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma. Diagn Interv Imaging 2021; 103:97-102. [PMID: 34666945 DOI: 10.1016/j.diii.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.
Collapse
Affiliation(s)
- Angèle Bonnin
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Carole Durot
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Manel Djelouah
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Florent Grange
- Department of Dermatology, Valence Hospital, 26000 Valence, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, APH-HP, 94000 Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Christine Hoeffel
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, Reims Champagne-Ardenne University, 51000 Reims, France.
| |
Collapse
|
67
|
Amini M, Nazari M, Shiri I, Hajianfar G, Deevband MR, Abdollahi H, Arabi H, Rahmim A, Zaidi H. Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma. Phys Med Biol 2021; 66. [PMID: 34544053 DOI: 10.1088/1361-6560/ac287d] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/20/2021] [Indexed: 12/23/2022]
Abstract
We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.
Collapse
Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, School of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver BC, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1211 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
68
|
Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis. Eur Radiol 2021; 32:2426-2436. [PMID: 34643781 DOI: 10.1007/s00330-021-08303-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/10/2021] [Accepted: 08/25/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC. METHODS In this retrospective study, 215 patients (average age: 57 years (18-87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set. RESULTS Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively. CONCLUSION FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions. KEY POINTS • Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.
Collapse
|
69
|
Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O'Connor JPB, Papanikolaou N, Messiou C, Koh DM, Orton MR. Radiomics in Oncology: A Practical Guide. Radiographics 2021; 41:1717-1732. [PMID: 34597235 PMCID: PMC8501897 DOI: 10.1148/rg.2021210037] [Citation(s) in RCA: 151] [Impact Index Per Article: 50.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Radiomics refers to the extraction of mineable data from medical imaging
and has been applied within oncology to improve diagnosis,
prognostication, and clinical decision support, with the goal of
delivering precision medicine. The authors provide a practical approach
for successfully implementing a radiomic workflow from planning and
conceptualization through manuscript writing. Applications in oncology
typically are either classification tasks that involve computing the
probability of a sample belonging to a category, such as benign versus
malignant, or prediction of clinical events with a time-to-event
analysis, such as overall survival. The radiomic workflow is
multidisciplinary, involving radiologists and data and imaging
scientists, and follows a stepwise process involving tumor segmentation,
image preprocessing, feature extraction, model development, and
validation. Images are curated and processed before segmentation, which
can be performed on tumors, tumor subregions, or peritumoral zones.
Extracted features typically describe the distribution of signal
intensities and spatial relationship of pixels within a region of
interest. To improve model performance and reduce overfitting, redundant
and nonreproducible features are removed. Validation is essential to
estimate model performance in new data and can be performed iteratively
on samples of the dataset (cross-validation) or on a separate hold-out
dataset by using internal or external data. A variety of noncommercial
and commercial radiomic software applications can be used. Guidelines
and artificial intelligence checklists are useful when planning and
writing up radiomic studies. Although interest in the field continues to
grow, radiologists should be familiar with potential pitfalls to ensure
that meaningful conclusions can be drawn. Online supplemental material is available for this
article. Published under a CC BY 4.0 license.
Collapse
Affiliation(s)
- Joshua D Shur
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Simon J Doran
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Santosh Kumar
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Derfel Ap Dafydd
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Kate Downey
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - James P B O'Connor
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Nikolaos Papanikolaou
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Christina Messiou
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Dow-Mu Koh
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Matthew R Orton
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| |
Collapse
|
70
|
Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
Collapse
|
71
|
Xing P, Chen L, Yang Q, Song T, Ma C, Grimm R, Fu C, Wang T, Peng W, Lu J. Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging 2021; 21:54. [PMID: 34579789 PMCID: PMC8477463 DOI: 10.1186/s40644-021-00423-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 09/03/2021] [Indexed: 11/24/2022] Open
Abstract
Background To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference. Methods Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves. Results The mean, median, 5th, and 95th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2WKurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC5th & T2WKurtosis parameters was also similar to that of the ADC5th & ADCDiff−Variance. Conclusions Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.
Collapse
Affiliation(s)
- Pengyi Xing
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Luguang Chen
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Qingsong Yang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Tao Song
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Robert Grimm
- Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Wenjia Peng
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China.
| |
Collapse
|
72
|
Liu S, Xu M, Qiao X, Ji C, Li L, Zhou Z. Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images. BMC Cancer 2021; 21:1038. [PMID: 34530755 PMCID: PMC8447770 DOI: 10.1186/s12885-021-08672-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/09/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC). METHODS The preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy. RESULTS The differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1-3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively. CONCLUSION We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.
Collapse
Affiliation(s)
- Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Xiangmei Qiao
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Changfeng Ji
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China.
| |
Collapse
|
73
|
Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
Collapse
Affiliation(s)
- A A Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D A Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A A Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F N Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| |
Collapse
|
74
|
Shaker R, Wilke C, Ober C, Lawrence J. Machine learning model development for quantitative analysis of CT heterogeneity in canine hepatic masses may predict histologic malignancy. Vet Radiol Ultrasound 2021; 62:711-719. [PMID: 34448312 DOI: 10.1111/vru.13012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 01/24/2023] Open
Abstract
Tumor heterogeneity is a well-established marker of biologically aggressive neoplastic processes and is associated with local recurrence and distant metastasis. Quantitative analysis of CT textural features is an indirect measure of tumor heterogeneity and therefore may help predict malignant disease. The purpose of this retrospective, secondary analysis study was to quantitatively evaluate CT heterogeneity in dogs with histologically confirmed liver masses to build a predictive model for malignancy. Forty dogs with liver tumors and corresponding histopathologic evaluation from a previous prospective study were included. Triphasic image acquisition was standardized across dogs and whole liver and liver mass were contoured on each precontrast and delayed postcontrast dataset. First-order and second-order indices were extracted from contoured regions. Univariate analysis identified potentially significant indices that were subsequently used for top-down model construction. Multiple quadratic discriminatory models were constructed and tested, including individual models using both postcontrast and precontrast whole liver or liver mass volumes. The best performing model utilized the CT features voxel volume and uniformity from postcontrast mass contours; this model had an accuracy of 0.90, sensitivity of 0.67, specificity of 1.0, positive predictive value of 1.0, negative predictive value of 0.88, and precision of 1.0. Heterogeneity indices extracted from delayed postcontrast CT hepatic mass contours were more informative about tumor type compared to indices from whole liver contours, or from precontrast hepatic mass and whole liver contours. Results demonstrate that CT radiomic feature analysis may hold clinical utility as a noninvasive method of predicting hepatic malignancy and may influence diagnostic or therapeutic approaches.
Collapse
Affiliation(s)
- Rami Shaker
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.,Medical School, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christopher Wilke
- Department of Radiation Oncology, Medical School, University of Minnesota, Minneapolis, Minnesota, USA.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christopher Ober
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
| | - Jessica Lawrence
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
| |
Collapse
|
75
|
Able H, Wolf-Ringwall A, Rendahl A, Ober CP, Seelig DM, Wilke CT, Lawrence J. Computed tomography radiomic features hold prognostic utility for canine lung tumors: An analytical study. PLoS One 2021; 16:e0256139. [PMID: 34403435 PMCID: PMC8370631 DOI: 10.1371/journal.pone.0256139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/29/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitative analysis of computed tomography (CT) radiomic features is an indirect measure of tumor heterogeneity, which has been associated with prognosis in human lung carcinoma. Canine lung tumors share similar features to human lung tumors and serve as a model in which to investigate the utility of radiomic features in differentiating tumor type and prognostication. The purpose of this study was to correlate first-order radiomic features from canine pulmonary tumors to histopathologic characteristics and outcome. Disease-free survival, overall survival time and tumor-specific survival were calculated as days from the date of CT scan. Sixty-seven tumors from 65 dogs were evaluated. Fifty-six tumors were classified as primary pulmonary adenocarcinomas and 11 were non-adenocarcinomas. All dogs were treated with surgical resection; 14 dogs received adjuvant chemotherapy. Second opinion histopathology in 63 tumors confirmed the histologic diagnosis in all dogs and further characterized 53 adenocarcinomas. The median overall survival time was longer (p = 0.004) for adenocarcinomas (339d) compared to non-adenocarcinomas (55d). There was wide variation in first-order radiomic statistics across tumors. Mean Hounsfield units (HU) ratio (p = 0.042) and median mean HU ratio (p = 0.042) were higher in adenocarcinomas than in non-adenocarcinomas. For dogs with adenocarcinoma, completeness of excision was associated with overall survival (p<0.001) while higher mitotic index (p = 0.007) and histologic score (p = 0.037) were associated with shorter disease-free survival. CT-derived tumor variables prognostic for outcome included volume, maximum axial diameter, and four radiomic features: integral total, integral total mean ratio, total HU, and max mean HU ratio. Tumor volume was also significantly associated with tumor invasion (p = 0.044). Further study of radiomic features in canine lung tumors is warranted as a method to non-invasively interrogate CT images for potential predictive and prognostic utility.
Collapse
Affiliation(s)
- Hannah Able
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- * E-mail: (HA); (JL)
| | - Amber Wolf-Ringwall
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Aaron Rendahl
- Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Christopher P. Ober
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Davis M. Seelig
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Chris T. Wilke
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
- Department of Radiation Oncology, Medical School, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Jessica Lawrence
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
- * E-mail: (HA); (JL)
| |
Collapse
|
76
|
Mao X, Guo Y, Wen F, Liang H, Sun W, Lu Z. Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE). Cancer Imaging 2021; 21:49. [PMID: 34384496 PMCID: PMC8359085 DOI: 10.1186/s40644-021-00418-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/19/2021] [Indexed: 12/15/2022] Open
Abstract
Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.
Collapse
Affiliation(s)
- Xiaonan Mao
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Yan Guo
- GE Healthcare (China), Shanghai, China
| | - Feng Wen
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Hongyuan Liang
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Wei Sun
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China
| | - Zaiming Lu
- Department of Radiology, ShengJing hospital of China Medical University, 12# floor at 1# building, 39 Huaxiang Road, Shenyang City, 110000, Liaoning Province, China.
| |
Collapse
|
77
|
Bracci S, Dolciami M, Trobiani C, Izzo A, Pernazza A, D'Amati G, Manganaro L, Ricci P. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol Med 2021; 126:1425-1433. [PMID: 34373989 PMCID: PMC8558266 DOI: 10.1007/s11547-021-01399-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 07/06/2021] [Indexed: 12/18/2022]
Abstract
Purpose The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC. Methods By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort. Results The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively. Conclusion Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.
Collapse
Affiliation(s)
- Stefano Bracci
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Claudio Trobiani
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Antonella Izzo
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Angelina Pernazza
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giulia D'Amati
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Paolo Ricci
- Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy.
| |
Collapse
|
78
|
Quantitative Histogram Analysis of T2-Weighted and Diffusion-Weighted Magnetic Resonance Images for Prediction of Malignant Thymic Epithelial Tumors. J Comput Assist Tomogr 2021; 45:795-801. [PMID: 34347704 DOI: 10.1097/rct.0000000000001197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE To assess the value of histogram analysis for differentiating a high-risk thymic epithelial tumor (TET) from a low-risk TET using T2-weighted images and the apparent diffusion coefficient (ADC). METHODS Forty-nine patients with histopathologically proven TET after thymectomy were enrolled in this study and retrospectively classified as having low-risk TET (low-risk thymoma) or high-risk TET (high-risk thymoma or thymic carcinoma). Twelve parameters were obtained from the quantitative histogram analysis. The histogram parameters were compared using the Mann-Whitney U test. Diagnostic efficacy was estimated by receiver-operating characteristic curve analysis. RESULTS Twenty-five patients were classified as having low-risk TET and 24 as having high-risk TET. The mean ADC value showed diagnostic efficacy for differentiating high-risk TET from low-risk TET, with an area under the curve of 0.7, and was better than when using conventional methods alone. CONCLUSION The ADC-based histogram analysis could help to differentiate between high-risk and low-risk TETs.
Collapse
|
79
|
Lee SH, Kao GD, Feigenberg SJ, Dorsey JF, Frick MA, Jean-Baptiste S, Uche CZ, Cengel KA, Levin WP, Berman AT, Aggarwal C, Fan Y, Xiao Y. Multiblock Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:1451-1465. [PMID: 33662459 PMCID: PMC8286285 DOI: 10.1016/j.ijrobp.2021.02.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/07/2021] [Accepted: 02/12/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE The main objective of the present study was to integrate 18F-FDG-PET/CT radiomics with multiblock discriminant analysis for predicting circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic body radiation therapy (SBRT). METHODS Fifty-six patients with stage I NSCLC treated with SBRT underwent 18F-FDG-PET/CT imaging pre-SBRT and post-SBRT (median, 5 months; range, 3-10 months). CTCs were assessed via a telomerase-based assay before and within 3 months after SBRT and dichotomized at 5 and 1.3 CTCs/mL. Pre-SBRT, post-SBRT, and delta PET/CT radiomics features (n = 1548 × 3/1562 × 3) were extracted from gross tumor volume. Seven feature blocks were constructed including clinical parameters (n = 12). Multiblock data integration was performed using block sparse partial least squares-discriminant analysis (sPLS-DA) referred to as Data Integration Analysis for Biomarker Discovery Using Latent Components (DIABLO) for identifying key signatures by maximizing common information between different feature blocks while discriminating CTC levels. Optimal input blocks were identified using a pairwise combination method. DIABLO performance for predicting pre-SBRT and post-SBRT CTCs was evaluated using combined AUC (area under the curve, averaged across different blocks) analysis with 20 × 5-fold cross-validation (CV) and compared with that of concatenation-based sPLS-DA that consisted of combining all features into 1 block. CV prediction scores between 1 class versus the other were compared using the Wilcoxon rank sum test. RESULTS For predicting pre-SBRT CTCs, DIABLO achieved the best performance with combined pre-SBRT PET radiomics and clinical feature blocks, showing CV AUC of 0.875 (P = .009). For predicting post-SBRT CTCs, DIABLO achieved the best performance with combined post-SBRT CT and delta CT radiomics feature blocks, showing CV AUCs of 0.883 (P = .001). In contrast, all single-block sPLS-DA models could not attain CV AUCs higher than 0.7. CONCLUSIONS Multiblock integration with discriminant analysis of 18F-FDG-PET/CT radiomics has the potential for predicting pre-SBRT and post-SBRT CTCs. Radiomics and CTC analysis may complement and together help guide the subsequent management of patients with ES-NSCLC.
Collapse
Affiliation(s)
- Sang Ho Lee
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Gary D Kao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Steven J Feigenberg
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jay F Dorsey
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa A Frick
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Samuel Jean-Baptiste
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Chibueze Z Uche
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Keith A Cengel
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - William P Levin
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail T Berman
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Charu Aggarwal
- Division of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| |
Collapse
|
80
|
Liu J, Pei Y, Zhang Y, Wu Y, Liu F, Gu S. Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features. Abdom Radiol (NY) 2021; 46:3748-3757. [PMID: 33386449 PMCID: PMC8286952 DOI: 10.1007/s00261-020-02891-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 11/29/2020] [Accepted: 12/04/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA). METHODS MRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan-Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS). RESULTS The optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat(1,- 1)T2 (P = 0.01) and SumEntrp(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803-0.949. Kaplan-Meier analyses further demonstrated that BCLC (P < 0.001), Correlat(1,- 1)T2 (P = 0.023) and SumEntrp(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS. CONCLUSIONS MR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.
Collapse
Affiliation(s)
- Jun Liu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan People’s Republic of China
- Xiangya Hospital, Central South University, Changsha, 410008 Hunan People’s Republic of China
| | - Yu Zhang
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Yifan Wu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Fuquan Liu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Shanzhi Gu
- Department of Interventional Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006 Hunan People’s Republic of China
| |
Collapse
|
81
|
Caruso D, Zerunian M, Daffina J, Polici M, Polidori T, Tipaldi MA, Ronconi E, Pucciarelli F, Lucertini E, Rossi M, Laghi A. Radiomics and functional imaging in lung cancer: the importance of radiological heterogeneity beyond FDG PET/CT and lung biopsy. Eur J Radiol 2021; 142:109874. [PMID: 34339955 DOI: 10.1016/j.ejrad.2021.109874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/24/2020] [Accepted: 07/21/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET/CT) has a central role in the lung nodules' characterization even if, with SUV < 2.5, percutaneous CT-guided Lung Biopsy (CTLB) is needed to assess nodule nature. In that scenario, CT Texture Analysis (CTTA) could be a non-invasive imaging biomarker. Our purpose is to test CTTA ability in differentiating malignant from benign nodules. METHOD Patients that underwent FDG PET/CT followed by CTLB between January 2013 and December 2018 were retrospectively enrolled. Were included patients with lung nodule SUV < 2.5 and histological diagnosis. EXCLUSION CRITERIA nodules SUV > 2.5, patients who refused CTLB or received oncological treatment before CTLB, indeterminate pathology report, CT motion artifacts. Two radiologists in consensus performed CTTA, drawing a volumetric Region of Interest of nodule with a dedicated first order TA software with and without spatial scaling filters, on preliminary CT performed for CTLB. Statistics included a comparison between malignant and benign neoplasms distribution (2-tailed T-test or Mann-Whitney test according to normal/non-normal data distribution), P-values < 0.05 were considered statistically significant. CTTA accuracy was tested with Receiver Operating Characteristics (ROC) curve. RESULTS Form an initial population of 1178, 46 patients encountered inclusion criteria. Pathologist reported 27/46 (59%) malignant and 19/46 (41%) benign nodules. In malignant lesions CTTA showed lower Kurtosis' and higher Skewness' values (all P ≤ 0.0013 and all filtered TA P < 0.024, respectively). ROC curve showed significant Area Under the Curve for Kurtosis and Skewness (0.654 and 0.642, P < 0.001) at medium filtration. CONCLUSIONS CTTA is a promising radiological tool to characterize benign and malignant lung nodules, even in those cases without an altered glucose metabolism.
Collapse
Affiliation(s)
- Damiano Caruso
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Marta Zerunian
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Julia Daffina
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Michela Polici
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Tiziano Polidori
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Marcello Andrea Tipaldi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Edoardo Ronconi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Francesco Pucciarelli
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Elena Lucertini
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Michele Rossi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| | - Andrea Laghi
- Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital Via di Grottarossa 1035-1039 00189 Rome, Italy.
| |
Collapse
|
82
|
Park S, Lee JM, Park J, Lee J, Bae JS, Kim JH, Joo I. Volumetric CT Texture Analysis of Intrahepatic Mass-Forming Cholangiocarcinoma for the Prediction of Postoperative Outcomes: Fully Automatic Tumor Segmentation Versus Semi-Automatic Segmentation. Korean J Radiol 2021; 22:1797-1808. [PMID: 34402247 PMCID: PMC8546140 DOI: 10.3348/kjr.2021.0055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/08/2021] [Accepted: 04/27/2021] [Indexed: 11/15/2022] Open
Abstract
Objective To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38–78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601–0.997 and 0.177–0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.
Collapse
Affiliation(s)
- Sungeun Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Konkuk University Medical Center, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jihyuk Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jae Seok Bae
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jae Hyun Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| |
Collapse
|
83
|
Pijl JP, Nienhuis PH, Kwee TC, Glaudemans AWJM, Slart RHJA, Gormsen LC. Limitations and Pitfalls of FDG-PET/CT in Infection and Inflammation. Semin Nucl Med 2021; 51:633-645. [PMID: 34246448 DOI: 10.1053/j.semnuclmed.2021.06.008] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
White blood cells activated by either a pathogen or as part of a systemic inflammatory disease are characterized by high energy consumption and are therefore taking up the glucose analogue PET tracer FDG avidly. It is therefore not surprising that a steadily growing body of research and clinical reports now supports the use of FDG PET/CT to diagnose a wide range of patients with non-oncological diseases. However, using FDG PET/CT in patients with infectious or inflammatory diseases has some limitations and potential pitfalls that are not necessarily as pronounced in oncology FDG PET/CT. Some of these limitations are of a general nature and related to the laborious acquisition of PET images in patients that are often acutely ill, whereas others are more disease-specific and related to the particular metabolism in some of the organs most commonly affected by infections or inflammatory disease. Both inflammatory and infectious diseases are characterized by a more diffuse and less pathognomonic pattern of FDG uptake than oncology FDG PET/CT and the affected organs also typically have some physiological FDG uptake. In addition, patients referred to PET/CT with suspected infection or inflammation are rarely treatment naïve and may have received varying doses of antibiotics, corticosteroids or other immune-modulating drugs at the time of their examination. Combined, this results in a higher rate of false positive FDG findings and also in some cases a lower sensitivity to detect active disease. In this review, we therefore discuss the limitations and pitfalls of FDG PET/CT to diagnose infections and inflammation taking these issues into consideration. Our review encompasses the most commonly encountered inflammatory and infectious diseases in head and neck, in the cardiovascular system, in the abdominal organs and in the musculoskeletal system. Finally, new developments in the field of PET/CT that may help overcome some of these limitations are briefly highlighted.
Collapse
Affiliation(s)
- Jordy P Pijl
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Pieter H Nienhuis
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Andor W J M Glaudemans
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Riemer H J A Slart
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen; Faculty of Science and Technology, Department of Biomedical Photonic Imaging, University of Twente, Enschede
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET Center, Aarhus University Hospital, Aarhus N.
| |
Collapse
|
84
|
Zhang C, Wen HL, Zhang R, Xie SY, Xie CM. Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study. Acta Radiol 2021; 63:1005-1013. [PMID: 34233501 DOI: 10.1177/02841851211029083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The relevance of Epstein-Barr virus (EBV) in gastric carcinoma has been represented by the existence of EBV-encoded small RNA (EBER) in the tumor cells and has prognostic significance in gastric cancer, while gastric adenocarcinoma represents the most frequently occurring gastric malignancy. PURPOSE To observe the capacity of radiomic features extracted from contrast-enhanced computed tomography (CE-CT) images to differentiate EBER-positive gastric adenocarcinoma from EBER-negative ones. MATERIAL AND METHODS A total of 54 patients with gastric adenocarcinoma (EBER-positive: 27, EBER-negative: 27) were retrospectively examined. Radiomic imaging features were extracted from all regions of interest (ROI) delineated by two experienced radiologists on late arterial phase CT images. We distinguished related radiomic features through the two-tailed t test and applied them to construct a decision tree model to evaluate whether EBER in situ hybridization positive had appeared. RESULTS Nine radiomics features were significantly related to EBER in situ hybridization status (P < 0.05), four of which were used to build the decision tree through backward elimination: Correlation_ AllDirection_offset7, Correlation_ angle135_offset7, RunLengthNonuniformity_ AllDirection_offset1_SD, and HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD. The decision tree model consisted of seven decision nodes and six terminal nodes, three of which demonstrated positive EBER in situ hybridization. The specificity, sensitivity, and accuracy of the model were 84%, 80%, and 81.7%, respectively. The area under the curve of the decision tree model was 0.87. CONCLUSION Radiomics based on CE-CT could be applied to predict EBER in situ hybridization status preoperatively in patients with gastric adenocarcinoma.
Collapse
Affiliation(s)
- Cheng Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Hai-lin Wen
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Rong Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Shu-yi Xie
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Chuan-miao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| |
Collapse
|
85
|
Chen Y, Li H, Feng J, Suo S, Feng Q, Shen J. A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease. J Inflamm Res 2021; 14:2731-2740. [PMID: 34194236 PMCID: PMC8238542 DOI: 10.2147/jir.s314912] [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: 04/08/2021] [Accepted: 06/08/2021] [Indexed: 12/23/2022] Open
Abstract
Purpose The prediction of the loss of response (LOR) to infliximab (IFX) is crucial for optimizing treatment strategies and shifting biologics. However, a secondary LOR is difficult to predict by endoscopy due to the intestinal stricture, perforation, and fistulas. This study aimed to develop and validate a radiomic nomogram for the prediction of secondary LOR to IFX in patients with Crohn’s disease (CD). Patients and Methods A total of 186 biologic-naive patients diagnosed with CD between September 2016 and June 2019 were enrolled. Secondary LOR was determined during week 54. Computed tomography enterography (CTE) texture analysis (TA) features were extracted from lesions and analyzed using LIFEx software. Feature selection was performed by least absolute shrinkage and selection operator (LASSO) and ten-fold cross validation. A nomogram was constructed using multivariable logistic regression, and the internal validation was approached by ten-fold cross validation. Results Predictors contained in the radiomics nomogram included three first-order and five second-order signatures. The prediction model presented significant discrimination (AUC, 0.880; 95% CI, 0.816–0.944) and high calibration (mean absolute error of = 0.028). Decision curve analysis (DCA) indicated that the nomogram provided clinical net benefit. Ten-fold cross validation assessed the stability of the nomogram with an AUC of 0.817 and an accuracy of 0.819. Conclusion This novel radiomics nomogram provides a predictive tool to assess secondary LOR to IFX in patients with Crohn’s disease. This tool will help physicians decide when to switch therapy.
Collapse
Affiliation(s)
- Yueying Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, 200127, People's Republic of China
| | - Hanyang Li
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, 200127, People's Republic of China
| | - Jing Feng
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, 200127, People's Republic of China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, People's Republic of China
| | - Qi Feng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, People's Republic of China
| | - Jun Shen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, 200127, People's Republic of China
| |
Collapse
|
86
|
Mazzei MA, Di Giacomo L, Bagnacci G, Nardone V, Gentili F, Lucii G, Tini P, Marrelli D, Morgagni P, Mura G, Baiocchi GL, Pittiani F, Volterrani L, Roviello F. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer). Quant Imaging Med Surg 2021; 11:2376-2387. [PMID: 34079708 DOI: 10.21037/qims-20-683] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC). Methods Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed. Results In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R2: 0.546 for the internal cohort and P=0.014, Nagelkerke R2: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763. Conclusions Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.
Collapse
Affiliation(s)
- Maria Antonietta Mazzei
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Letizia Di Giacomo
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Giulio Bagnacci
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | | | - Francesco Gentili
- Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Gabriele Lucii
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Daniele Marrelli
- Department of Medical, Surgical and Neuro Sciences, Unit of Surgical Oncology, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Paolo Morgagni
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Gianni Mura
- Department of Surgery, San Donato Hospital, Arezzo, Italy
| | - Gian Luca Baiocchi
- Department of Clinical and Experimental Studies, Surgical Clinic, University of Brescia, Brescia, Italy
| | - Frida Pittiani
- Department of Radiology, ASST Spedali Civili Brescia, Brescia, Italy
| | - Luca Volterrani
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Franco Roviello
- Department of Medical, Surgical and Neuro Sciences, Unit of Surgical Oncology, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| |
Collapse
|
87
|
Caruso D, Zerunian M, Pucciarelli F, Bracci B, Polici M, D’Arrigo B, Polidori T, Guido G, Barbato L, Polverari D, Benvenga A, Iannicelli E, Laghi A. Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients. Diagnostics (Basel) 2021; 11:diagnostics11061000. [PMID: 34072633 PMCID: PMC8229560 DOI: 10.3390/diagnostics11061000] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 02/07/2023] Open
Abstract
Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10–100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.
Collapse
|
88
|
Ganeshan B, Miles K, Afaq A, Punwani S, Rodriguez M, Wan S, Walls D, Hoy L, Khan S, Endozo R, Shortman R, Hoath J, Bhargava A, Hanson M, Francis D, Arulampalam T, Dindyal S, Chen SH, Ng T, Groves A. Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer. Cancers (Basel) 2021; 13:2715. [PMID: 34072712 PMCID: PMC8199380 DOI: 10.3390/cancers13112715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 01/07/2023] Open
Abstract
To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42-83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: rs = -0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients.
Collapse
Affiliation(s)
- Balaji Ganeshan
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Kenneth Miles
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Asim Afaq
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Shonit Punwani
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Manuel Rodriguez
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Simon Wan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Darren Walls
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Luke Hoy
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Saif Khan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Raymond Endozo
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Robert Shortman
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - John Hoath
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Aman Bhargava
- Institute of Health Barts and London Medical School, Queen Mary University of London (QMUL), London E1 2AD, UK;
| | - Matthew Hanson
- Division of Cancer and Clinical Support, Barking, Havering and Redbridge University Hospitals NHS Trust, Queens and King George Hospitals, Essex IG3 8YB, UK;
| | - Daren Francis
- Department of Colorectal Surgery, Royal Free London NHS Foundation Trust, Barnet and Chase Farm Hospitals, London NW3 2QG, UK;
| | - Tan Arulampalam
- Department of Surgery, East Suffolk and North Essex NHS Foundation Trust, Colchester General Hospital, Colchester CO4 5JL, UK;
| | - Sanjay Dindyal
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Shih-Hsin Chen
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Tony Ng
- School of Cancer & Pharmaceutical Sciences, King’s College London (KCL), London WC2R 2LS, UK;
| | - Ashley Groves
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| |
Collapse
|
89
|
Pei X, Wang P, Ren JL, Yin XP, Ma LY, Wang Y, Ma X, Gao BL. Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas. Front Oncol 2021; 11:659969. [PMID: 34123817 PMCID: PMC8187849 DOI: 10.3389/fonc.2021.659969] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/28/2021] [Indexed: 01/03/2023] Open
Abstract
Purpose This study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas. Materials and Methods CT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer. Results A total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence. Conclusion Radiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.
Collapse
Affiliation(s)
- Xu Pei
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Ping Wang
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Jia-Liang Ren
- Department of Pharmaceutical Diagnostics, GE Healthcare China (Shanghai) Co Ltd., Shanghai, China
| | - Xiao-Ping Yin
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China.,Key Laboratory of Cancer Radiotherapy and Chemotherapy Mechanism and Regulations, Baoding, China
| | - Lu-Yao Ma
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Yun Wang
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Xi Ma
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Bu-Lang Gao
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| |
Collapse
|
90
|
Liu Y, Fan H, Dong D, Liu P, He B, Meng L, Chen J, Chen C, Lang J, Tian J. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer. Transl Oncol 2021; 14:101113. [PMID: 33975178 PMCID: PMC8131712 DOI: 10.1016/j.tranon.2021.101113] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/14/2022] Open
Abstract
The metastatic status of lymph nodes in cervical cancer patients can be predicted. Computed tomography-based radiomic model can identify the status of the normal-sized lymph node singly. The model may help doctors to make staging and clinical decision, and realize individualized treatment.
Purpose Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. Methods A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). Results Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. Conclusion We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.
Collapse
Affiliation(s)
- Yujia Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Huijian Fan
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Di Dong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai 519000, China.
| | - Ping Liu
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Bingxi He
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Lingwei Meng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Jiaming Chen
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Chunlin Chen
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Jinghe Lang
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing 100730, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai 519000, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China.
| |
Collapse
|
91
|
Zhang MH, Hasse A, Carroll T, Pearson AT, Cipriani NA, Ginat DT. Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics. Gland Surg 2021; 10:1646-1654. [PMID: 34164309 DOI: 10.21037/gs-20-830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background The purpose of this study is to determine if Haralick texture analysis on CT imaging of mucoepidermoid carcinomas (MEC) can differentiate low-grade and high-grade tumors. Methods A retrospective review of 18 patients with MEC of the salivary glands, corresponding CT imaging and pathology report was performed. Tumors were manually segmented and image analysis was performed to calculate radiomic features. Radiomic features were compared between low-grade and high-grade MEC. A multivariable logistic regression model and receiver operating characteristic analysis was performed. Results A total of 18 patients (mean age, 51, range 9-83 years, 8 men and 10 women) were included. Nine patients had low-grade pathology and nine patients had high-grade pathology. Of the 18 cases, 7 (39%) occurred in the parotid gland and 11 (61%) occurred in minor salivary glands. No individual feature was significantly different between low-grade and high-grade MEC. A logistic regression model including surface regularity, energy and information measure II of correlation was performed and was able to predict high-grade MEC accurately (sensitivity 89%, specificity 68%). The area under the receiver operating characteristic curve was 0.802. Conclusions High-grade MEC tend to have a low energy, high correlation texture as well as surface irregularity. Together, these three features may comprise a tumor phenotype that is able to predict high-grade pathology in MECs.
Collapse
Affiliation(s)
- Michael H Zhang
- Pritzker School of Medicine, The University of Chicago, Chicago IL, USA
| | - Adam Hasse
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | - Timothy Carroll
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | | | | | - Daniel T Ginat
- Department of Radiology, The University of Chicago, Chicago IL, USA
| |
Collapse
|
92
|
Razik A, Das CJ, Sharma R, Malla S, Sharma S, Seth A, Srivastava DN. Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study. Br J Radiol 2021; 94:20201114. [PMID: 33882245 DOI: 10.1259/bjr.20201114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC). METHODS After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRADTM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann-Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson's test was used to identify redundancy in the results. RESULTS All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors. CONCLUSION MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion. ADVANCES IN KNOWLEDGE There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.
Collapse
Affiliation(s)
- Abdul Razik
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Chandan J Das
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Raju Sharma
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Sundeep Malla
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Sanjay Sharma
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Amlesh Seth
- Departments of Urology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Deep Narayan Srivastava
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| |
Collapse
|
93
|
Li N, Wang L, Hu Y, Han W, Zheng F, Song W, Jiang J. Global evolution of research on pulmonary nodules: a bibliometric analysis. Future Oncol 2021; 17:2631-2645. [PMID: 33880950 DOI: 10.2217/fon-2020-0987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.
Collapse
Affiliation(s)
- Ning Li
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Lei Wang
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yaoda Hu
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wei Han
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Fuling Zheng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jingmei Jiang
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| |
Collapse
|
94
|
Kong C, Zhao Z, Chen W, Lv X, Shu G, Ye M, Song J, Ying X, Weng Q, Weng W, Fang S, Chen M, Tu J, Ji J. Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE. Eur Radiol 2021; 31:7500-7511. [PMID: 33860832 PMCID: PMC8452577 DOI: 10.1007/s00330-021-07910-0] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 02/23/2021] [Accepted: 03/18/2021] [Indexed: 12/13/2022]
Abstract
Objectives To develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients. Materials Ninety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results The AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusion The radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis. Key Points • The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07910-0.
Collapse
Affiliation(s)
- Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Zhongwei Zhao
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Xiuling Lv
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Gaofeng Shu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Miaoqing Ye
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Jingjing Song
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Xihui Ying
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Wei Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Shiji Fang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China
| | - Jianfei Tu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Department of Radiology, Affiliated Lishui Hospital of Zhejiang University/the Fifth Affiliated Hospital of Wenzhou Medical University/The Central Hospital of Zhejiang Lishui, Lishui, 323000, China.
| |
Collapse
|
95
|
Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma. Sci Rep 2021; 11:6933. [PMID: 33767315 PMCID: PMC7994625 DOI: 10.1038/s41598-021-86497-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 03/16/2021] [Indexed: 12/31/2022] Open
Abstract
To explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.
Collapse
|
96
|
Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040588. [PMID: 33806029 PMCID: PMC8064478 DOI: 10.3390/diagnostics11040588] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
This study developed a pretreatment CT-based radiomic model of lymph node response to induction chemotherapy in locally advanced head and neck squamous cell carcinoma (HNSCC) patients. This was a single-center retrospective study of patients with locally advanced HPV+ HNSCC. Forty-one enlarged lymph nodes were found from 27 patients on pretreatment CT and were split into 3:1 training and testing cohorts. Ninety-three radiomic features were extracted. A radiomic model and a combined radiomic-clinical model predicting lymph node response to induction chemotherapy were developed using multivariable logistic regression. Median age was 57 years old, and 93% of patients were male. Post-treatment evaluation was 32 days after treatment, with a median reduction in lymph node volume of 66%. A three-feature radiomic model (minimum, skewness, and low gray level run emphasis) and a combined radiomic-clinical model were developed. The combined model performed the best, with AUC = 0.85 on the training cohort and AUC = 0.75 on the testing cohort. A pretreatment CT-based lymph node radiomic signature combined with clinical parameters was able to predict nodal response to induction chemotherapy for patients with locally advanced HNSCC.
Collapse
|
97
|
Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation. Life (Basel) 2021; 11:life11030264. [PMID: 33806817 PMCID: PMC8005065 DOI: 10.3390/life11030264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND To explore the potential role of computed tomography (CT) texture analysis and an imaging biomarker in differentiating primary gastro-intestinal lymphoma (PGIL) from gastro-intestinal adenocarcinoma (GIAC). METHODS A total of 131 patients with surgical pathologically PGIL and GIAC were enrolled in this study. Histogram parameters of arterial and venous phases extracted from contrast enhanced modified discrete cosine transform (MDCT) images were compared between PGIL and GIAC by Mann-Whitney U tests. The optimal parameters for differentiating these two groups were obtained through receiver operating characteristic (ROC) curves and the area under the curve (AUC) was calculated. RESULTS Compared with GIAC, in arterial phase, PGIL had statistically higher 5th, 10th percentiles (p = 0.003 and 0.011) and statistically lower entropy (p = 0.001). In the venous phase, PGIL had statistically lower mean, median, 75th, 90th, 95th percentiles, and entropy (p = 0.036, 0.029, 0.007, 0.001 and 0.001, respectively). For differentiating PGIL from GIAC, V-median + A-5th percentile was an optimal parameter for combined diagnosis (AUC = 0.746, p < 0.0001), and the corresponding sensitivity and specificity were 81.7 and 64.8%, respectively. CONCLUSION CT texture analysis could be useful for differential diagnosis of PGIL and GIAC.
Collapse
|
98
|
Guo Y, Chen X, Lin X, Chen L, Shu J, Pang P, Cheng J, Xu M, Sun Z. Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study. Eur Radiol 2021; 31:7067-7076. [PMID: 33755755 DOI: 10.1007/s00330-021-07768-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 01/21/2021] [Accepted: 02/09/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs). METHODS We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature. RESULTS The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively. CONCLUSIONS A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs. KEY POINTS • The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.
Collapse
Affiliation(s)
- Yifan Guo
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China
| | - Xiaojun Chen
- Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, 365 Renmin East Road, Jinhua, 321000, China
| | - Xianda Lin
- Department of Neurology, The Wenzhou Third Clinical Institute Affiliated To Wenzhou Medical University, 299 Gu'an Road, Wenzhou, 325000, China
| | - Litian Chen
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, 109 Xueyuan West Road, Wenzhou, 325000, China
| | - Jiner Shu
- Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, 365 Renmin East Road, Jinhua, 321000, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, 122 Shuguang Road, Hangzhou, 310000, China
| | - Jianmin Cheng
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, 109 Xueyuan West Road, Wenzhou, 325000, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China.
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China.
| |
Collapse
|
99
|
Kayı Cangır A, Dizbay Sak S, Güneş G, Orhan K. Differentiation of benign and malignant regions in paraffin embedded tissue blocks of pulmonary adenocarcinoma using micro CT scanning of paraffin tissue blocks: a pilot study for method validation. Surg Today 2021; 51:1594-1601. [PMID: 33646412 DOI: 10.1007/s00595-021-02252-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/17/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Micro computed tomography (micro-CT) can provide detailed information about the internal structure of materials. This study aimed to demonstrate the diagnostic value of micro-CT in formalin fixed paraffin embedded pulmonary adenocarcinomas by correlating the micro-CT findings of tumoral and non-tumoral areas with hematoxylin and eosin (HE) sections. METHODS Paraffin blocks obtained from three adenocarcinomas were scanned with micro-CT. Ten regions of interest (ROIs) from adenocarcinoma and 11 ROIs from pulmonary parenchyma (ROI-C and ROI-N, respectively) areas were compared regarding the various structural parameters. RESULTS All parameters were significantly different regarding the tumoral and non-tumoral ROIs. The percent object volume, structure thickness, structure linear density, connectivity and connectivity density were higher in ROI-Cs (p < 0.000, p < 0.000, p = 0.001, p < 0.000, and p < 0.000 respectively); whereas intersection surface and structure model index were higher in ROI-Ns (p < 0.000 and p < 0.000). The open porosity percentage was higher in ROI-Ns (68.86 + 2.96 vs 48.29 + 5.11, p < 0.000) and the closed porosity percentage was higher in ROI-Cs (2.29 + 0.55 vs 0.57 + 0.17 p < 0.000). CONCLUSIONS The tumoral and non-tumoral areas in paraffin blocks can be distinguished from each other, using the quantitative and qualitative information obtained by micro-CT. Making this distinction with quantitative data obtained from micro-CT can therefore be the basis of creating artificial intelligence algorithms in the future.
Collapse
Affiliation(s)
- Ayten Kayı Cangır
- FEBTS, Department of Thoracic Surgery, Faculty of Medicine, Ankara University, Ankara University Medical Design Application and Research Center (MEDITAM), Sıhhiye, 06230, Ankara, Turkey.
| | - Serpil Dizbay Sak
- Department of Pathology, Faculty of Medicine, Ankara University, Sıhhiye, 06230, Ankara, Turkey
| | - Gökalp Güneş
- Department of Thoracic Surgery, Faculty of Medicine, Ankara University, Sıhhiye, 06230, Ankara, Turkey
| | - Kaan Orhan
- Deparment of Dentoaxillofacial, Radiology Faculty of Dentistry, Ankara University, Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| |
Collapse
|
100
|
Liu J, Yin P, Wang S, Liu T, Sun C, Hong N. CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors. Front Oncol 2021; 11:628534. [PMID: 33718203 PMCID: PMC7953900 DOI: 10.3389/fonc.2021.628534] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/13/2021] [Indexed: 01/08/2023] Open
Abstract
Objectives This study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs). Methods A total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models. Results Of the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483). Conclusions NECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better. Advances in Knowledge Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.
Collapse
Affiliation(s)
- Jin Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Sicong Wang
- Pharmaceutical Diagnostic Team, GE Healthcare, Shanghai, China
| | - Tao Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Chao Sun
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| |
Collapse
|