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Patkulkar P, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis. ACS OMEGA 2023; 8:6126-6138. [PMID: 36844580 PMCID: PMC9948167 DOI: 10.1021/acsomega.2c06659] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.
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Nannini S, Penel N, Bompas E, Willaume T, Kurtz JE, Gantzer J. Shortening the Time Interval for the Referral of Patients With Soft Tissue Sarcoma to Expert Centers Using Mobile Health: Retrospective Study. JMIR Mhealth Uhealth 2022; 10:e40718. [PMID: 36350680 PMCID: PMC9685503 DOI: 10.2196/40718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 09/17/2022] [Accepted: 10/06/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND According to guidelines, all patients with sarcoma must be managed from initial diagnosis at expert sarcoma centers. However, in everyday practice, the time interval to an expert center visit can be long, which delays presentation to an expert multidisciplinary tumor board and increases the risk of inappropriate management, negatively affecting local tumor control and prognosis. The advent of mobile health offers an easy way to facilitate communication and cooperation between general health care providers (eg, general practitioners and radiologists) and sarcomas experts. We developed a mobile app (Sar'Connect) based on the algorithm designed by radiologists from the French Sarcoma Group. Through a small number of easy-to-answer questions, Sar'Connect provides personalized advice for the management of patients and contact information for the closest expert center. OBJECTIVE This retrospective study is the first to assess this mobile app's potential benefits in reducing the time interval for patient referral to an expert center according to the initial clinical characteristics of the soft tissue tumor. METHODS From May to December 2021, we extracted tumor mass data for 78 patients discussed by the multidisciplinary tumor boards at 3 centers of the French Sarcoma Group. We applied the Sar'Connect algorithm to these data and estimated the time interval between the first medical description of the soft tissue mass and the referral to expert center. We then compared this estimated time interval with the observed time interval. RESULTS We found that the use of Sar'Connect could potentially shorten the time interval to an expert center by approximately 7.5 months (P<.001). Moreover, for half (31/60, 52%) of the patients with a malignant soft tissue tumor, Sar'Connect could have avoided inappropriate management outside of the reference center. We did not identify a significant determinant for shortening the time interval for referral. CONCLUSIONS Overall, promoting the use of a simple mobile app is an innovative and straightforward means to potentially accelerate both the referral and management of patients with soft tissue sarcoma at expert centers.
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Affiliation(s)
- Simon Nannini
- Department of Medical Oncology, Strasbourg-Europe Cancer Institute, Strasbourg, France
| | - Nicolas Penel
- Department of Medical Oncology, Center Oscar Lambret, Lille University, Lille, France
| | - Emmanuelle Bompas
- Department of Medical Oncology, Institut de Cancérologie de l'Ouest, Nancy, France
| | - Thibault Willaume
- Department of Radiology, University Hospital of Strasbourg, Strasbourg, France
| | - Jean-Emmanuel Kurtz
- Department of Medical Oncology, Strasbourg-Europe Cancer Institute, Strasbourg, France
| | - Justine Gantzer
- Department of Medical Oncology, Strasbourg-Europe Cancer Institute, Strasbourg, France
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Ling Z, Li X, Wu G, Fadoul H. Radiomics of CTA is feasible in identifying muscle ischemia. Acta Radiol 2022; 64:1469-1475. [PMID: 36050936 DOI: 10.1177/02841851221119884] [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: 11/16/2022]
Abstract
BACKGROUND Advanced models based on computed tomography angiography (CTA) radiomics features in discriminating muscle ischemia from normal condition are lacking. PURPOSE To investigate the feasibility of radiomics of CTA in discriminating ischemic muscle from normal muscle. MATERIAL AND METHODS A total of 102 patients (51 ischemia and 51 non-ischemia) were analyzed using a CTA radiomics method. The radiomics features of muscle were compared between ischemic and normal cases. The maximum relevance minimum redundancy (mRMR) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression model were used. The receiver operating characteristic (ROC) curve was used to determine the performance of radiomics signature. RESULTS Thirty-nine CTA radiomics features were significantly different between the two groups (P < 0.05). By LASSO, six features were used to construct a model. The signature area under the curve was 0.92 and 0.91 in the training and validation cohorts, respectively. The sensitivity and specificity of the signature were 92% and 86% for the training cohort, and 80% and 94% for the validation cohort, respectively. CONCLUSION CTA radiomics signature is useful in identifying ischemic muscle in selected patients.
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Affiliation(s)
- Zhiyu Ling
- Department of Radiology, The first People's Hospital of Yongkang, Yongkang, Zhejiang, PR China
| | - Xiaoming Li
- Department of Radiology, 66375Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, PR China
| | - Gang Wu
- Department of Radiology, 66375Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, PR China
| | - Hissein Fadoul
- Department of Radiology, 66375Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, PR China
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Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, Imbriaco M, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 2021; 12:68. [PMID: 34076740 PMCID: PMC8172744 DOI: 10.1186/s13244-021-01008-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/05/2021] [Indexed: 02/07/2023] Open
Abstract
Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
| | - Renato Cuocolo
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | | | - Lorenzo Carlo Pescatori
- Assistance Publique - Hôpitaux de Paris (AP-HP), Service d'Imagerie Médicale, CHU Henri Mondor, Créteil, France
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Massimo Imbriaco
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Xiao B, Wang P, Zhao Y, Liu Y, Ye Z. Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia. Medicine (Baltimore) 2021; 100:e24955. [PMID: 33663135 PMCID: PMC7909173 DOI: 10.1097/md.0000000000024955] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/09/2020] [Accepted: 02/04/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH).Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann-Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis.The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05).ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.
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Affiliation(s)
| | - Peiguo Wang
- Department of Radiotherapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Surgical Management of Pelvic Sarcomas. Sarcoma 2021. [DOI: 10.1007/978-981-15-9414-4_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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Lv M, Zhou Z, Tang Q, Xu J, Huang Q, Lu L, Duan S, Zhu J, Li H. Differentiation of usual vertebral compression fractures using CT histogram analysis as quantitative biomarkers: A proof-of-principle study. Eur J Radiol 2020; 131:109264. [PMID: 32920220 DOI: 10.1016/j.ejrad.2020.109264] [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: 05/18/2020] [Revised: 07/19/2020] [Accepted: 08/24/2020] [Indexed: 01/20/2023]
Abstract
PURPOSE To investigate the utility of CT histogram analysis (CTHA) for discrimination of traumatic, osteoporotic and malignant fractures in patients with vertebral compression fractures (VCFs). To evaluate the feasibility and accuracy of CTHA in differentiating non-malignant (traumatic and osteoporotic) from malignant VCFs. MATERIALS AND METHODS Totally, 235 patients with VCFs were enrolled in the current experimental study. There were 132 patients with traumatic VCFs, 51 with osteoporotic VCFs and 52 with malignant VCFs, with MRI and histology as the standard references. All the patients underwent unenhanced CT scans. Nineteen histogram-based parameters were derived using Omni-Kinetics software (Omni-Kinetics, GE Healthcare). The reproducibility of those parameters was evaluated using two independent delineations conducted by two observers. These histogram parameters were compared among the three different VCFs using Kruskal-Wallis H test. Traumatic VCFs and osteoporotic VCFs were combined as non-malignant VCFs and compared with malignant VCFs using Mann-Whitney U test Multivariable logistic regression analysis was performed on the significantly different features and built a diagnosis model. Receiver operating characteristic (ROC) curve was carried out to observe the difference of diagnostic performance between the single positive parameter and the combination of parameters. RESULTS All the 19 parameters presented excellent reproducibility, with intraclass correlation coefficient values from 0.789 to 0.997. At quantitative evaluation, the best predictive histogram parameters in discrimination of the three different types of VCFs were relative min intensity (p = 0.022), relative entropy (p = 0.043), and relative frequency size (p < 0.001). Relative frequency size (p < 0.001) and relative quantile5 (p = 0.012) resulted in statistically significant difference between non-malignant and malignant VCFs. The area under ROC curve indicated that relative frequency size combined with relative quantile5 (0.754; 95 % confidence intervals: 0.661∼0.829; p < 0.001) was of best performance in differentiating malignant from non-malignant VCFs. CONCLUSIONS Our results are encouraging and suggest that histogram parameters derived from unenhanced CT could be reliable quantitative biomarkers for diff ;erential diagnosis of usual VCFs.
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Affiliation(s)
- Mu Lv
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China
| | - Zhichao Zhou
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China
| | - Qingkun Tang
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
| | - Jie Xu
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
| | - Qiao Huang
- Department of Radiology, Mayo Clinic, Rochester, United States
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, United States
| | | | - Jianguo Zhu
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China.
| | - Haige Li
- The Second Clinical Medical College of Nanjing Medical University, Nan Jing, China; Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nan Jing, China
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