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Hatamikia S, George G, Schwarzhans F, Mahbod A, Woitek R. Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation? Comput Struct Biotechnol J 2024; 23:52-63. [PMID: 38125296 PMCID: PMC10730996 DOI: 10.1016/j.csbj.2023.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.
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Affiliation(s)
- Sepideh Hatamikia
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
- Austrian Center for Medical Innovation and Technology (ACMIT), Viktor Kaplan-Straße 2/1, Wiener Neustadt 2700, Austria
| | - Geevarghese George
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Florian Schwarzhans
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Amirreza Mahbod
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Ramona Woitek
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
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Castellana R, Fanni SC, Roncella C, Romei C, Natrella M, Neri E. Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Eur J Radiol 2024; 176:111510. [PMID: 38781919 DOI: 10.1016/j.ejrad.2024.111510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated. RESULTS Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively). CONCLUSION Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies.
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Affiliation(s)
- Roberto Castellana
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy.
| | - Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
| | - Claudia Roncella
- Radiology Unit, Apuane Hospital, Azienda USL Toscana Nord Ovest, Via Mattei 21, 54100, Massa, Italy
| | - Chiara Romei
- Department of Diagnostic Imaging, Diagnostic Radiology 2, Pisa University Hospital, Via Paradisa 2, 56124, Pisa, Italy
| | - Massimiliano Natrella
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
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Scicolone R, Vacca S, Pisu F, Benson JC, Nardi V, Lanzino G, Suri JS, Saba L. Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque. Eur J Radiol 2024; 176:111497. [PMID: 38749095 DOI: 10.1016/j.ejrad.2024.111497] [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: 03/16/2024] [Revised: 04/14/2024] [Accepted: 05/03/2024] [Indexed: 06/17/2024]
Abstract
Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.
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Affiliation(s)
- Roberta Scicolone
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy
| | - Sebastiano Vacca
- University of Cagliari, School of Medicine and Surgery, Cagliari, Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy
| | - John C Benson
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Valentina Nardi
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy.
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Zoccali C, Mallamaci F, Lightstone L, Jha V, Pollock C, Tuttle K, Kotanko P, Wiecek A, Anders HJ, Remuzzi G, Kalantar-Zadeh K, Levin A, Vanholder R. A new era in the science and care of kidney diseases. Nat Rev Nephrol 2024; 20:460-472. [PMID: 38575770 DOI: 10.1038/s41581-024-00828-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 04/06/2024]
Abstract
Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.
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Affiliation(s)
- Carmine Zoccali
- Kidney Research Institute, New York City, NY, USA.
- Institute of Molecular Biology and Genetics (Biogem), Ariano Irpino, Italy.
- Associazione Ipertensione Nefrologia Trapianto Kidney (IPNET), c/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy.
| | - Francesca Mallamaci
- Nephrology, Dialysis and Transplantation Unit Azienda Ospedaliera "Bianchi-Melacrino-Morelli", Reggio Calabria, Italy
- CNR-IFC, Institute of Clinical Physiology, Research Unit of Clinical Epidemiology and Physiopathology of Kidney Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy
| | - Liz Lightstone
- Department of Immunology and Inflammation, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK
| | - Vivek Jha
- George Institute for Global Health, UNSW, New Delhi, India
- School of Public Health, Imperial College, London, UK
- Prasanna School of Public Health, Manipal Academy of Medical Education, Manipal, India
| | - Carol Pollock
- Kolling Institute, Royal North Shore Hospital University of Sydney, Sydney, NSW, Australia
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest, Spokane, Washington, USA
- Department of Medicine, University of Washington, Seattle, Spokane, Washington, USA
- Kidney Research Institute, Institute of Translational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Peter Kotanko
- Kidney Research Institute, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrzej Wiecek
- Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia, 40-027, Katowice, Poland
| | - Hans Joachim Anders
- Division of Nephrology, Department of Medicine IV, Hospital of the Ludwig Maximilians University Munich, Munich, Germany
| | - Giuseppe Remuzzi
- Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Bergamo, Italy
| | - Kamyar Kalantar-Zadeh
- Harold Simmons Center for Kidney Disease Research and Epidemiology, California, USA
- Division of Nephrology and Hypertension, University of California Irvine, School of Medicine, Orange, Irvine, USA
- Veterans Affairs Healthcare System, Division of Nephrology, Long Beach, California, USA
| | - Adeera Levin
- University of British Columbia, Vancouver General Hospital, Division of Nephrology, Vancouver, British Columbia, Canada
- British Columbia, Provincial Kidney Agency, Vancouver, British Columbia, Canada
| | - Raymond Vanholder
- European Kidney Health Alliance, Brussels, Belgium
- Nephrology Section, Department of Internal Medicine and Paediatrics, University Hospital Ghent, Ghent, Belgium
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Guo Z, Zhao M, Liu Z, Zheng J, Gong Y, Huang L, Xue J, Zhou X, Li S. Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. PLoS Negl Trop Dis 2024; 18:e0012235. [PMID: 38870200 DOI: 10.1371/journal.pntd.0012235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.
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Affiliation(s)
- Zhaoyu Guo
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Miaomiao Zhao
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Zhenhua Liu
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Jinxin Zheng
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfeng Gong
- School of Public Health, Fudan University, Shanghai, China
| | - Lulu Huang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Jingbo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaonong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shizhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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6
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Ma D, Zhou T, Chen J, Chen J. Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. BMC Med Imaging 2024; 24:144. [PMID: 38867143 PMCID: PMC11170881 DOI: 10.1186/s12880-024-01278-5] [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: 01/26/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer. METHODS We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc. RESULTS Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant. CONCLUSION Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.
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Affiliation(s)
- Dong Ma
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Teli Zhou
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
- Yibicom Health Management, Guangzhou, Guangdong, 510700, China
| | - Jing Chen
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Jun Chen
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China.
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Ramireddy JK, Sathya A, Sasidharan BK, Varghese AJ, Sathyamurthy A, John NO, Chandramohan A, Singh A, Joel A, Mittal R, Masih D, Varghese K, Rebekah G, Ram TS, Thomas HMT. Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment? J Gastrointest Cancer 2024:10.1007/s12029-024-01073-z. [PMID: 38856797 DOI: 10.1007/s12029-024-01073-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/11/2024]
Abstract
OBJECTIVE(S) The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study, we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points: (1) before the start of any treatment using baseline T2-weighted MRI (T2W-MR) and (2) at the start of radiation treatment using planning CT. METHODS Patients on nCRT followed by surgery between June 2017 to December 2019 were included in the study. Histopathological tumour response grading (TRG) was used for classification, and gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance. Four machine learning classifiers built clinical, radiomic, and merged models. Model performances were evaluated on a held-out test dataset following 3-fold cross-validation using area under the receiver operator characteristic curves (AUC) with bootstrap 95% confidence intervals. RESULTS One hundred and fifty patients were included; 58/150 with TRG 1 were classified as complete responders, and rest were incomplete responders (IR). Clinical models performed better (AUC = 0.68) compared to radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR model showed best performance (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT-merged models could only achieve the highest AUC of 0.66. CONCLUSION Merging clinical and baseline T2W-MR radiomics enhances predicting pathological response in rectal cancer. Validation in larger cohorts is warranted, especially for watch and wait strategies.
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Grants
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
- Fluid research major grant Christian Medical College, Vellore
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Affiliation(s)
- Jeba Karunya Ramireddy
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - A Sathya
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Balu Krishna Sasidharan
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Amal Joseph Varghese
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Arvind Sathyamurthy
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Neenu Oliver John
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | | | - Ashish Singh
- Department of Medical Oncology, Christian Medical College, Vellore, India
| | - Anjana Joel
- Department of Medical Oncology, Christian Medical College, Vellore, India
| | - Rohin Mittal
- Department of General Surgery, Christian Medical College, Vellore, India
| | - Dipti Masih
- Department of Pathology, Christian Medical College, Vellore, India
| | - Kripa Varghese
- Department of Pathology, Christian Medical College, Vellore, India
| | - Grace Rebekah
- Department of Biostatistics, Christian Medical College, Vellore, India
| | - Thomas Samuel Ram
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Hannah Mary T Thomas
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India.
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Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga AI, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer HP, Schlett CL, Maier-Hein K, Bamberg F. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics. Insights Imaging 2024; 15:124. [PMID: 38825600 PMCID: PMC11144687 DOI: 10.1186/s13244-024-01704-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/20/2024] [Indexed: 06/04/2024] Open
Abstract
OBJECTIVES Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
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Affiliation(s)
- Ralf Floca
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany.
| | - Jonas Bohn
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Faculty of Bioscience, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christian Haux
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TU Munich University Hospital, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, TU Munich, Munich, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Annika Reinke
- Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Weiß
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Nolden
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Albert
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Tobias Norajitra
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Marc Dewey
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin Institute of Health, DZHK (German Centre for Cardiovascular Research), and DKTK (German Cancer Consortium), both partner sites Berlin, Berlin, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
- Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
| | - Martin Büchert
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Eva Maria Fallenberg
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich (FZJ), Juelich, Germany
- Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Bonn, Cologne & Duesseldorf, Germany
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Faculty 3, Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Tobias Haueise
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Marco Janoschke
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Matthias Jung
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Lena Sophie Kiefer
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jürgen Machann
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | | | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Barbara D Wichtmann
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Wenzhao Zhao
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heinz-Peter Schlemmer
- German Cancer Research Center (DKFZ) Heidelberg, Division of Radiology, Heidelberg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
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9
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Levenson RM, Singh Y, Rieck B, Hathaway QA, Farrelly C, Rozenblit J, Prasanna P, Erickson B, Choudhary A, Carlsson G, Sarkar D. Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology. J Transl Med 2024; 104:102060. [PMID: 38626875 DOI: 10.1016/j.labinv.2024.102060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024] Open
Abstract
Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.
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Affiliation(s)
- Richard M Levenson
- Department of Pathology and Laboratory Medicine, University of California Davis, Davis, California.
| | - Yashbir Singh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota.
| | - Bastian Rieck
- Helmholtz Munich and Technical University of Munich, Munich, Germany
| | - Quincy A Hathaway
- Department of Medical Education, West Virginia University, Morgantown, West Virginia
| | | | | | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | | | | | - Gunnar Carlsson
- Department of Mathematics, Stanford University, Stanford, California
| | - Deepa Sarkar
- Institute of Genomic Health, Ichan school of Medicine, Mount Sinai, New York
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10
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Davey MS, Davey MG, Kenny P, Gheiti AJC. The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee-a systematic review and meta-analysis. Ir J Med Sci 2024:10.1007/s11845-024-03714-5. [PMID: 38822185 DOI: 10.1007/s11845-024-03714-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/14/2024] [Indexed: 06/02/2024]
Abstract
The primary aim of this study was to systematically review current literature evaluating the use of radiomics in establishing the role of magnetic resonance imaging (MRI) findings in native knees in predicting features of osteoarthritis (OA). A systematic review was performed with respect to PRISMA guidelines in search of studies reporting radiomic analysis of magnetic resonance imaging (MRI) to analyse patients with native knee OA. Sensitivity and specificity of radiomic analyses were included for meta-analysis. Following our initial literature search of 1271 studies, only 5 studies met our inclusion criteria. This included 1730 patients (71.5% females) with a mean age of 55.4 ± 15.6 years (range 24-66). The mean RQS of included studies was 16.6 (11-21). Meta-analysis demonstrated the pooled sensitivity and specificity for MRI in predicting features of OA in patients with native knees were 0.74 (95% CI 0.71, 0.78) and 0.85 (95% CI 0.83, 0.87), respectively. The results of this systematic review suggest that the high sensitivities and specificity of MRI-based radiomics may represent potential biomarker in the early identification and classification of native knee OA. Such analysis may inform surgeons to facilitate earlier non-operative management of knee OA in the select pre-symptomatic patients, prior to clinical or radiological evidence of degenerative change.
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Affiliation(s)
- Martin S Davey
- Connolly Hospital Blanchardstown, Dublin, Ireland.
- National Orthopaedic Hospital Cappagh, Dublin, Ireland.
- Royal College of Surgeons in Ireland, Dublin, Ireland.
| | | | - Paddy Kenny
- Connolly Hospital Blanchardstown, Dublin, Ireland
- National Orthopaedic Hospital Cappagh, Dublin, Ireland
- Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Adrian J Cassar Gheiti
- Connolly Hospital Blanchardstown, Dublin, Ireland
- National Orthopaedic Hospital Cappagh, Dublin, Ireland
- Royal College of Surgeons in Ireland, Dublin, Ireland
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11
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Ponsiglione A, Gambardella M, Stanzione A, Green R, Cantoni V, Nappi C, Crocetto F, Cuocolo R, Cuocolo A, Imbriaco M. Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis. Eur Radiol 2024; 34:3981-3991. [PMID: 37955670 PMCID: PMC11166859 DOI: 10.1007/s00330-023-10427-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/10/2023] [Accepted: 09/27/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction. MATERIALS AND METHODS Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity. RESULTS Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76. CONCLUSION MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed. CLINICAL RELEVANCE STATEMENT Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements. KEY POINTS • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | | | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy.
| | - Roberta Green
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Valeria Cantoni
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Carmela Nappi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Felice Crocetto
- Department of Neurosciences, Human Reproduction and Odontostomatology, University of Naples Federico II, Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
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12
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Zhang Y, Zou Y, Tan W, Lv C. Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images. Acta Radiol 2024; 65:632-640. [PMID: 38591947 DOI: 10.1177/02841851241244507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
BACKGROUND The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). PURPOSE Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. MATERIAL AND METHODS A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman's correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. RESULTS A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. CONCLUSION The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.
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Affiliation(s)
- Yumei Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yuefen Zou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wenfeng Tan
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Chengyin Lv
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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13
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Rajgor AD, Kui C, McQueen A, Cowley J, Gillespie C, Mill A, Rushton S, Obara B, Bigirumurame T, Kallas K, O'Hara J, Aboagye E, Hamilton DW. Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study. J Laryngol Otol 2024; 138:685-691. [PMID: 38095096 PMCID: PMC11096831 DOI: 10.1017/s0022215123002372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/18/2023] [Accepted: 11/02/2023] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Advanced laryngeal cancers are clinically complex; there is a paucity of modern decision-making models to guide tumour-specific management. This pilot study aims to identify computed tomography-based radiomic features that may predict survival and enhance prognostication. METHODS Pre-biopsy, contrast-enhanced computed tomography scans were assembled from a retrospective cohort (n = 72) with advanced laryngeal cancers (T3 and T4). The LIFEx software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and grey-level zone length matrix - grey-level non-uniformity (tumour heterogeneity) were selected via least absolute shrinkage and selection operator-based Cox regression and explored for prognostic potential. RESULTS A greater shape compacity (hazard ratio 2.89) and grey-level zone length matrix - grey-level non-uniformity (hazard ratio 1.64) were significantly associated with worse 5-year disease-specific survival (p < 0.05). Cox regression models yielded a superior C-index when incorporating radiomic features (0.759) versus clinicopathological variables alone (0.655). CONCLUSIONS Two radiomic features were identified as independent prognostic biomarkers. A multi-centre prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers.
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Affiliation(s)
- Amarkumar Dhirajlal Rajgor
- Newcastle University, Newcastle-Upon-Tyne, UK
- Population Health Sciences Institute, Newcastle University, Newcastle-Upon-Tyne, UK
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
| | - Christopher Kui
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
| | - Andrew McQueen
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
| | - Josh Cowley
- Newcastle University, Newcastle-Upon-Tyne, UK
| | | | - Aileen Mill
- Newcastle University, Newcastle-Upon-Tyne, UK
| | | | | | | | - Khaled Kallas
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
| | - James O'Hara
- Newcastle University, Newcastle-Upon-Tyne, UK
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
| | - Eric Aboagye
- Imperial College London Cancer Imaging Centre, Department of Surgery & Cancer, Hammersmith Hospital, London, UK
| | - David Winston Hamilton
- Newcastle University, Newcastle-Upon-Tyne, UK
- Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Freeman Road, Newcastle-Upon-Tyne, UK
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14
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Gotta J, Gruenewald LD, Martin SS, Booz C, Mahmoudi S, Eichler K, Gruber-Rouh T, Biciusca T, Reschke P, Juergens LJ, Onay M, Herrmann E, Scholtz JE, Sommer CM, Vogl TJ, Koch V. From pixels to prognosis: Imaging biomarkers for discrimination and outcome prediction of pulmonary embolism : Original Research Article. Emerg Radiol 2024; 31:303-311. [PMID: 38523224 PMCID: PMC11130040 DOI: 10.1007/s10140-024-02216-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/11/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE Recent advancements in medical imaging have transformed diagnostic assessments, offering exciting possibilities for extracting biomarker-based information. This study aims to investigate the capabilities of a machine learning classifier that incorporates dual-energy computed tomography (DECT) radiomics. The primary focus is on discerning and predicting outcomes related to pulmonary embolism (PE). METHODS The study included 131 participants who underwent pulmonary artery DECT angiography between January 2015 and March 2022. Among them, 104 patients received the final diagnosis of PE and 27 patients served as a control group. A total of 107 radiomic features were extracted for every case based on DECT imaging. The dataset was divided into training and test sets for model development and validation. Stepwise feature reduction identified the most relevant features, which were used to train a gradient-boosted tree model. Receiver operating characteristics analysis and Cox regression tests assessed the association of texture features with overall survival. RESULTS The trained machine learning classifier achieved a classification accuracy of 0.94 for identifying patients with acute PE with an area under the receiver operating characteristic curve of 0.91. Radiomics features could be valuable for predicting outcomes in patients with PE, demonstrating strong prognostic capabilities in survival prediction (c-index, 0.991 [0.979-1.00], p = 0.0001) with a median follow-up of 130 days (IQR, 38-720). Notably, the inclusion of clinical or DECT parameters did not enhance predictive performance. CONCLUSION In conclusion, our study underscores the promising potential of leveraging radiomics on DECT imaging for the identification of patients with acute PE and predicting their outcomes. This approach has the potential to improve clinical decision-making and patient management, offering efficiencies in time and resources by utilizing existing DECT imaging without the need for an additional scoring system.
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Affiliation(s)
- Jennifer Gotta
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany.
- University Hospital Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany.
| | | | - Simon S Martin
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Christian Booz
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | | | - Katrin Eichler
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | | | - Teodora Biciusca
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Philipp Reschke
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | | | - Melis Onay
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Eva Herrmann
- Institut for Biostatistics and Mathematic Modelling, Goethe University Frankfurt, Frankfurt, 60590, Germany
| | - Jan-Erik Scholtz
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Christof M Sommer
- Clinic of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas J Vogl
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Vitali Koch
- Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
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15
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Baeßler B, Engelhardt S, Hekalo A, Hennemuth A, Hüllebrand M, Laube A, Scherer C, Tölle M, Wech T. Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging. Circ Cardiovasc Imaging 2024; 17:e015490. [PMID: 38889216 DOI: 10.1161/circimaging.123.015490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Sandy Engelhardt
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Amar Hekalo
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany (A. Hennemuth)
| | - Markus Hüllebrand
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Ann Laube
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Clemens Scherer
- Department of Medicine I, LMU University Hospital, LMU Munich, Germany (C.S.)
- Munich Heart Alliance, German Center for Cardiovascular Research (DZHK), Germany (C.S.)
| | - Malte Tölle
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
- Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Germany (T.W.)
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16
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Yao J, van der Pol CB. Editorial for "Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI-Based Radiomics Models". J Magn Reson Imaging 2024; 59:2033-2034. [PMID: 37578322 DOI: 10.1002/jmri.28952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Affiliation(s)
- Jason Yao
- Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Christian B van der Pol
- Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
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Yan T, Wang L, Chen X, Yin H, He W, Liu J, Liu S, Li X, Wang Y, Peng L. Predicting Left Ventricular Adverse Remodeling After Transcatheter Aortic Valve Replacement: A Radiomics Approach. Acad Radiol 2024:S1076-6332(24)00238-1. [PMID: 38821814 DOI: 10.1016/j.acra.2024.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 06/02/2024]
Abstract
RATIONALE AND OBJECTIVES To develop a radiomics model based on cardiac computed tomography (CT) for predicting left ventricular adverse remodeling (LVAR) in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS Patients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis. RESULTS A total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis. CONCLUSION The cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.
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Affiliation(s)
- Tingli Yan
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Radiology, Chengdu Universal Dicom Medical Imaging Diagnostic Center, Chengdu, China
| | - Lujing Wang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaoyi Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Hongkun Yin
- Infervision Medical Technology 9Co., Ltd, Beijing, China
| | - Wenzhang He
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jing Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Shengmei Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xue Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yinqiu Wang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Liqing Peng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
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Shahram MA, Azimian H, Abbasi B, Ganji Z, Khadem-Reza ZK, Khakshour E, Zare H. Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images. BMC Neurosci 2024; 25:26. [PMID: 38789970 PMCID: PMC11127326 DOI: 10.1186/s12868-024-00871-2] [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: 11/24/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
INTRODUCTION The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). MATERIAL AND METHOD MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia. RESULTS The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. CONCLUSION The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.
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Affiliation(s)
- Mohammad Amin Shahram
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hosein Azimian
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Bita Abbasi
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khandan Khadem-Reza
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Elham Khakshour
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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Kocak B, Borgheresi A, Ponsiglione A, Andreychenko AE, Cavallo AU, Stanzione A, Doniselli FM, Vernuccio F, Triantafyllou M, Cannella R, Trotta R, Ghezzo S, Akinci D'Antonoli T, Cuocolo R. Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol Exp 2024; 8:72. [PMID: 38740707 DOI: 10.1186/s41747-024-00471-z] [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: 03/18/2024] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at https://radiomic.github.io/CLEAR-E3/ .
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey.
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Anna E Andreychenko
- Laboratory for Digital Public Health Technologies, ITMO University, St. Petersburg, Russian Federation
| | - Armando Ugo Cavallo
- Division of Radiology, Istituto Dermopatico dell'Immacolata (IDI) IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Fabio M Doniselli
- Neuroradiology Unit, Fondazione Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milano, Italy
| | - Federica Vernuccio
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, 90127, Palermo, Italy
| | - Matthaios Triantafyllou
- Department of Medical Imaging, University Hospital of Heraklion, 71110, Crete, Voutes, Greece
| | - Roberto Cannella
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Romina Trotta
- Department of Radiology - Fatima Hospital, Seville, Spain
| | | | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
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20
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Paetkau O, Weppler S, Quon HC, Tchistiakova E, Kirkby C. Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy. Biomed Phys Eng Express 2024; 10:045014. [PMID: 38697028 DOI: 10.1088/2057-1976/ad4651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Background and purpose. To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy.Materials and methods. Training (n = 64) and testing (n = 23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints.Results. A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity = 80 ± 27% / balanced accuracy = 71 ± 23%, testing: sensitivity = 80 ± 10% / balanced accuracy = 73 ± 11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia.Conclusion. Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.
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Affiliation(s)
- Owen Paetkau
- Department of Physics and Astronomy, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
| | - Sarah Weppler
- Tom Baker Cancer Center, 1331 29 St NW, Calgary, AB, T2N 4N2, Canada
| | - Harvey C Quon
- Tom Baker Cancer Center, 1331 29 St NW, Calgary, AB, T2N 4N2, Canada
| | - Ekaterina Tchistiakova
- Department of Physics and Astronomy, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
| | - Charles Kirkby
- Department of Physics and Astronomy, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
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Koçak B, Yüzkan S, Mutlu S, Karagülle M, Kala A, Kadıoğlu M, Solak S, Sunman Ş, Temiz ZH, Ganiyusufoğlu AK. Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: in vivo experiments on discretization and resampling parameters. Diagn Interv Radiol 2024; 30:152-162. [PMID: 38073244 PMCID: PMC11095065 DOI: 10.4274/dir.2023.232543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/14/2023] [Indexed: 05/15/2024]
Abstract
PURPOSE To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features. METHODS The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90). RESULTS Image preprocessing parameters had a significant impact on the segmentation-based reproducibility of radiomic features. The bin width method yielded more reproducible features than the bin count method. In discretization experiments using the bin width on both sequences, according to the ICC cut-off values of 0.75 and 0.90, the rate of reproducible features ranged from 70% to 84% and from 35% to 57%, respectively, with an increasing percentage trend as parameter values decreased (from 84 to 5 for T2; 100 to 6 for T1ce). In the resampling experiments, these ranged from 53% to 74% and from 10% to 20%, respectively, with an increasing percentage trend from lower to higher parameter values (physical voxel size; from 1 x 1 x 1 to 2 x 2 x 2 mm3). CONCLUSION The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.
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Affiliation(s)
- Burak Koçak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sabahattin Yüzkan
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Samet Mutlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Karagülle
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ahmet Kala
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Kadıoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sıla Solak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Şeyma Sunman
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Zişan Hayriye Temiz
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ali Kürşad Ganiyusufoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
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Song H, Wang X, Wu R, Liu W. The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01825-8. [PMID: 38740709 DOI: 10.1007/s11547-024-01825-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). METHODS The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). RESULTS Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. CONCLUSIONS To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.
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Affiliation(s)
- Honghao Song
- Department of Pediatric Surgery, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, People's Republic of China
| | - Xiaoqing Wang
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
- Post-doctoral Research Station of Clinical Medicine, Liaocheng People's Hospital, Liaocheng, 252004, Shandong, People's Republic of China
| | - Rongde Wu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
| | - Wei Liu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China.
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Wu Y, Cao F, Lei H, Zhang S, Mei H, Ni L, Pang J. Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study. Abdom Radiol (NY) 2024:10.1007/s00261-024-04351-3. [PMID: 38733392 DOI: 10.1007/s00261-024-04351-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors. MATERIALS AND METHODS In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach. RESULTS A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method. CONCLUSIONS The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.
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Affiliation(s)
- Yaohai Wu
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Fei Cao
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hanqi Lei
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Shiqiang Zhang
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hongbing Mei
- Department of Urology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Liangchao Ni
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jun Pang
- Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
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Veeturi SS, Saleem A, Ojeda D, Sagues E, Sanchez S, Gudino A, Levy EI, Hasan D, Siddiqui AH, Tutino VM, Samaniego EA. Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms. RESEARCH SQUARE 2024:rs.3.rs-4350156. [PMID: 38766264 PMCID: PMC11100888 DOI: 10.21203/rs.3.rs-4350156/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs. Methods Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' demographic information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set. Results A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE Mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity and 73% specificity). Conclusions Combining AWE quantification through radiomic analysis with patient demographic data in a clinical nomogram achieved high accuracy in detecting symptomatic IAs.
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Committeri U, Barone S, Arena A, Fusco R, Troise S, Maffia F, Tramontano S, Bonavolontà P, Abbate V, Granata V, Elefante A, Ugga L, Giovacchini F, Salzano G, Califano L, Dell'Aversana Orabona G. New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101912. [PMID: 38719192 DOI: 10.1016/j.jormas.2024.101912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 05/06/2024] [Indexed: 05/19/2024]
Abstract
This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (DCs), Odontogenic Keratocysts (OKCs), and Unicystic Ameloblastomas (UAs). This retrospective study involves 103 patients who underwent jaw lesion surgery in the Maxillofacial Surgery Unit of Federico II University Of Naples between January 2018 and January 2023. Nonparametric Wilcoxon-Mann-Whitney and Kruskal Wallis tests were used for continuous variables. Linear and non-logistic regression models (LRM and NLRM) were employed, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarkers achieved an accuracy of 95 % and an AUC of 0.96. The accuracy of single radiomics predictors was lower than that of inflammatory biomarkers, with an AUC of 0.83. The Fine Tree model, utilizing NLR, SII, and one radiomic feature, achieved an accuracy of 94.3 % (AUC = 0.95) on the training and testing sets, and a validation set accuracy of 100 %. The Fine Tree model demonstrated the capability to discriminate between OKCs, UAs, and DCs. However, the LRM utilizing four inflammatory biomarkers proved to be the most effective algorithm for distinguishing between OKCs, UAs, and DCs.
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Affiliation(s)
- Umberto Committeri
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Simona Barone
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Antonio Arena
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Naples 80013, Italy
| | - Stefania Troise
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy.
| | - Francesco Maffia
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Sara Tramontano
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Paola Bonavolontà
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Vincenzo Abbate
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Naples 80131, Italy
| | - Andrea Elefante
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, Naples 80131, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, Naples 80131, Italy
| | - Francesco Giovacchini
- Department of Maxillo-Facial Medicine Surgery, Hospital of Perugia, Perugia 06132, Italy
| | - Giovanni Salzano
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Luigi Califano
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
| | - Giovanni Dell'Aversana Orabona
- Maxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via Sergio Pansini 5, Naples 80131, Italy
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Jiménez-Gaona Y, Álvarez MJR, Castillo-Malla D, García-Jaen S, Carrión-Figueroa D, Corral-Domínguez P, Lakshminarayanan V. BraNet: a mobil application for breast image classification based on deep learning algorithms. Med Biol Eng Comput 2024:10.1007/s11517-024-03084-1. [PMID: 38693328 DOI: 10.1007/s11517-024-03084-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/26/2024] [Indexed: 05/03/2024]
Abstract
Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.
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Affiliation(s)
- Yuliana Jiménez-Gaona
- Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador.
- Instituto de Instrumentación para la Imagen Molecular I3M, Universitat Politécnica de Valencia, 46022, Valencia, Spain.
- Theoretical and Experimental Epistemology Lab, School of Opto ΩN2L3G1, Waterloo, Canada.
| | - María José Rodríguez Álvarez
- Instituto de Instrumentación para la Imagen Molecular I3M, Universitat Politécnica de Valencia, 46022, Valencia, Spain
| | - Darwin Castillo-Malla
- Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador
- Instituto de Instrumentación para la Imagen Molecular I3M, Universitat Politécnica de Valencia, 46022, Valencia, Spain
- Theoretical and Experimental Epistemology Lab, School of Opto ΩN2L3G1, Waterloo, Canada
| | - Santiago García-Jaen
- Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador
| | | | - Patricio Corral-Domínguez
- Corporación Médica Monte Sinaí-CIPAM (Centro Integral de Patología Mamaria) Cuenca-Ecuador, Facultad de Ciencias Médicas, Universidad de Cuenca, Cuenca, 010203, Ecuador
| | - Vasudevan Lakshminarayanan
- Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L3G1, Canada
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Frood R, Mercer J, Brown P, Appelt A, Mistry H, Kochhar R, Scarsbrook A. Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma. Eur Radiol 2024; 34:3194-3204. [PMID: 37924344 PMCID: PMC11126458 DOI: 10.1007/s00330-023-10340-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/20/2023] [Accepted: 08/24/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVES The incidence of anal squamous cell carcinoma (ASCC) is increasing worldwide, with a significant proportion of patients treated with curative intent having recurrence. The ability to accurately predict progression-free survival (PFS) and overall survival (OS) would allow for development of personalised treatment strategies. The aim of the study was to train and external test radiomic/clinical feature derived time-to-event prediction models. METHODS Consecutive patients with ASCC treated with curative intent at two large tertiary referral centres with baseline FDG PET-CT were included. Radiomic feature extraction was performed using LIFEx software on the pre-treatment PET-CT. Two distinct predictive models for PFS and OS were trained and tuned at each of the centres, with the best performing models externally tested on the other centres' patient cohort. RESULTS A total of 187 patients were included from centre 1 (mean age 61.6 ± 11.5 years, median follow up 30 months, PFS events = 57/187, OS events = 46/187) and 257 patients were included from centre 2 (mean age 62.6 ± 12.3 years, median follow up 35 months, PFS events = 70/257, OS events = 54/257). The best performing model for PFS and OS was achieved using a Cox regression model based on age and metabolic tumour volume (MTV) with a training c-index of 0.7 and an external testing c-index of 0.7 (standard error = 0.4). CONCLUSIONS A combination of patient age and MTV has been demonstrated using external validation to have the potential to predict OS and PFS in ASCC patients. CLINICAL RELEVANCE STATEMENT A Cox regression model using patients' age and metabolic tumour volume showed good predictive potential for progression-free survival in external testing. The benefits of a previous radiomics model published by our group could not be confirmed on external testing. KEY POINTS • A predictive model based on patient age and metabolic tumour volume showed potential to predict overall survival and progression-free survival and was validated on an external test cohort. • The methodology used to create a predictive model from age and metabolic tumour volume was repeatable using external cohort data. • The predictive ability of positron emission tomography-computed tomography-derived radiomic features diminished when the influence of metabolic tumour volume was accounted for.
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Affiliation(s)
- Russell Frood
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
| | - Joseph Mercer
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, UK
| | - Peter Brown
- Department of Radiology, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK
| | - Ane Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Hitesh Mistry
- Division of Pharmacy, University of Manchester, Manchester, UK
| | - Rohit Kochhar
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, UK
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Wang H, Yu C, Ding H, Zhang L, Chen X, He L. Computed Tomography-Based Radiomics Signature for Predicting Segmental Chromosomal Aberrations at 1p36 and 11q23 in Pediatric Neuroblastoma. J Comput Assist Tomogr 2024; 48:472-479. [PMID: 38013242 DOI: 10.1097/rct.0000000000001564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVE This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma. METHODS Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI). RESULTS Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated. CONCLUSIONS Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.
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Affiliation(s)
- Haoru Wang
- From the Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Kim J, Choi YH, Yoon H, Lim HJ, Han JW, Lee MJ. Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study. Yonsei Med J 2024; 65:293-301. [PMID: 38653568 PMCID: PMC11045346 DOI: 10.3349/ymj.2023.0192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE This study aimed to predict high-risk neuroblastoma among neuroblastic tumors using radiomics features extracted from MRI. MATERIALS AND METHODS Pediatric patients (age≤18 years) diagnosed with neuroblastic tumors who had pre-treatment MR images available were enrolled from institution A from January 2010 to November 2019 (training set) and institution B from January 2016 to January 2022 (test set). Segmentation was performed with regions of interest manually drawn along tumor margins on the slice with the widest tumor area by two radiologists. First-order and texture features were extracted and intraclass correlation coefficients (ICCs) were calculated. Multivariate logistic regression (MLR) and random forest (RF) models from 10-fold cross-validation were built using these features. The trained MLR and RF models were tested in an external test set. RESULTS Thirty-two patients (M:F=23:9, 26.0±26.7 months) were in the training set and 14 patients (M:F=10:4, 33.4±20.4 months) were in the test set with radiomics features (n=930) being extracted. For 10 of the most relevant features selected, intra- and inter-observer variability was moderate to excellent (ICCs 0.633-0.911, 0.695-0.985, respectively). The area under the receiver operating characteristic curve (AUC) was 0.94 (sensitivity 67%, specificity 91%, and accuracy 84%) for the MLR model and the average AUC was 0.83 (sensitivity 44%, specificity 87%, and accuracy 75%) for the RF model from 10-fold cross-validation. In the test set, AUCs of the MLR and RF models were 0.94 and 0.91, respectively. CONCLUSION An MRI-based radiomics model can help predict high-risk neuroblastoma among neuroblastic tumors.
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Affiliation(s)
- Jisoo Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Haesung Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Ji Lim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jung Woo Han
- Department of Pediatric Hematology-Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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Zhao W, Hu Z, Kazerooni AF, Körzdörfer G, Nittka M, Davatzikos C, Viswanath SE, Wang X, Badve C, Ma D. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Invest Radiol 2024; 59:359-371. [PMID: 37812483 PMCID: PMC10997475 DOI: 10.1097/rli.0000000000001026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. MATERIALS AND METHODS A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. RESULTS First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. CONCLUSIONS The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.
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Affiliation(s)
- Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Zheyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Wang L, Guo T, Wang L, Yang W, Wang J, Nie J, Cui J, Jiang P, Li J, Zhang H. Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography. Heliyon 2024; 10:e29331. [PMID: 38644848 PMCID: PMC11033096 DOI: 10.1016/j.heliyon.2024.e29331] [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: 01/06/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/23/2024] Open
Abstract
Rationale and objectives Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methods A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. Results The SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer-Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. Conclusions Deep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.
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Affiliation(s)
- Lingjie Wang
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Tiedan Guo
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Li Wang
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Wentao Yang
- Basic Medical College, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
| | - Jingying Wang
- Department of Endemic Disease Prevention and Control, Shanxi Province Disease Prevention and Control Center, Shanxi Province, 030001, China
| | - Jianlong Nie
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai City, 200030, China
| | - Jingjing Cui
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai City, 200030, China
| | - Pengbo Jiang
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai City, 200030, China
| | - Junlin Li
- Department of Imaging Medicine, Inner Mongolia Autonomous Region People's Hospital, Hohhot, 010017, China
| | - Hua Zhang
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China
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Rashmitha, Manjunath KN, Kulkarni A, Kulkarni V. Segmentation and Volumetric Analysis of Heart from Cardiac CT Images. Cardiovasc Eng Technol 2024:10.1007/s13239-024-00715-4. [PMID: 38689094 DOI: 10.1007/s13239-024-00715-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/02/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis. METHOD This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest. RESULTS The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29 mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots. CONCLUSION The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.
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Affiliation(s)
- Rashmitha
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - K N Manjunath
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Anjali Kulkarni
- Consultant in Radiation Oncology, Clinical Informatics and Artificial Intelligence, Karkinos Healthcare, Bengaluru, India
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Khodabakhshi Z, Motisi L, Bink A, Broglie MA, Rupp NJ, Fleischmann M, von der Grün J, Guckenberger M, Tanadini-Lang S, Balermpas P. MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle". Sci Rep 2024; 14:9945. [PMID: 38688932 PMCID: PMC11061101 DOI: 10.1038/s41598-024-60200-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance.
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Affiliation(s)
- Zahra Khodabakhshi
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | - Laura Motisi
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradadiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martina A Broglie
- Department of Otorhinolaryngology, Zurich University Hospital, Zurich, Switzerland
| | - Niels J Rupp
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Maximilian Fleischmann
- Department of Radiation Oncology, J.W. Goethe University Hospital Frankfurt, Frankfurt, Germany
| | - Jens von der Grün
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | | | | | - Panagiotis Balermpas
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland.
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Reis EP, Blankemeier L, Zambrano Chaves JM, Jensen MEK, Yao S, Truyts CAM, Willis MH, Adams S, Amaro E, Boutin RD, Chaudhari AS. Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm. Eur Radiol 2024:10.1007/s00330-024-10769-6. [PMID: 38683384 DOI: 10.1007/s00330-024-10769-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. MATERIALS AND METHODS Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT". RESULTS The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification. CONCLUSION An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability. CLINICAL RELEVANCE STATEMENT Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging. KEY POINTS Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.
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Affiliation(s)
- Eduardo Pontes Reis
- Department of Radiology, Stanford University, Stanford, CA, USA.
- Center for Artificial Intelligence in Medicine & Imaging (AIMI), Stanford University, Stanford, CA, USA.
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil.
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Juan Manuel Zambrano Chaves
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Sally Yao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | | | - Marc H Willis
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Scott Adams
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Edson Amaro
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | - Robert D Boutin
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Santoro M, Zybin V, Coada CA, Mantovani G, Paolani G, Di Stanislao M, Modolon C, Di Costanzo S, Lebovici A, Ravegnini G, De Leo A, Tesei M, Pasquini P, Lovato L, Morganti AG, Pantaleo MA, De Iaco P, Strigari L, Perrone AM. Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study. Cancers (Basel) 2024; 16:1570. [PMID: 38672651 PMCID: PMC11048510 DOI: 10.3390/cancers16081570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/07/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. METHODS Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. RESULTS A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CONCLUSIONS CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.
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Affiliation(s)
- Miriam Santoro
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Vladislav Zybin
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | | | - Giulia Mantovani
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Giulia Paolani
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Marco Di Stanislao
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Cecilia Modolon
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Stella Di Costanzo
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, 400347 Cluj-Napoca, Romania;
- Surgical Specialties Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Gloria Ravegnini
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
| | - Antonio De Leo
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Marco Tesei
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Pietro Pasquini
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Luigi Lovato
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Alessio Giuseppe Morganti
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Maria Abbondanza Pantaleo
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Pierandrea De Iaco
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Anna Myriam Perrone
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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Yan J, Wang L, Pan L, Ye H, Zhu X, Feng Q, Ding Z, Ge X, Shi L. Analyzing the risk factors of unilateral trigeminal neuralgia under neurovascular compression. Front Hum Neurosci 2024; 18:1349186. [PMID: 38699563 PMCID: PMC11064654 DOI: 10.3389/fnhum.2024.1349186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/01/2024] [Indexed: 05/05/2024] Open
Abstract
Background This study aimed to explore the risk factors and potential causes of unilateral classical or idiopathic trigeminal neuralgia (C-ITN) by comparing patients and healthy controls (HCs) with neurovascular compression (NVC) using machine learning (ML). Methods A total of 84 C-ITN patients and 78 age- and sex-matched HCs were enrolled. We assessed the trigeminal pons angle and identified the compressing vessels and their location and severity. Machine learning was employed to analyze the cisternal segment of the trigeminal nerve (CN V). Results Among the C-ITN patients, 53 had NVC on the unaffected side, while 25 HCs exhibited bilateral NVC, and 24 HCs showed unilateral NVC. By comparing the cisternal segment of CN V between C-ITN patients on the affected side and HCs with NVC, we identified the side of NVC, the compressing vessel, and certain texture features as risk factors for C-ITN. Additionally, four texture features differed in the structure of the cisternal segment of CN V between C-ITN patients on the unaffected side and HCs with NVC. Conclusion Our findings suggest that the side of NVC, the compressing vessel, and the microstructure of the cisternal segment of CN V are associated with the risk of C-ITN. Furthermore, microstructural changes observed in the cisternal segment of CN V on the unaffected side of C-ITN patients with NVC indicate possible indirect effects on the CN V to some extent.
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Affiliation(s)
- Juncheng Yan
- Department of Rehabilitation, Hangzhou First People's Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Lei Pan
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Haiqi Ye
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Xiaofen Zhu
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Qi Feng
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Lei Shi
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Hangzhou, China
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Watzenboeck ML, Beer L, Kifjak D, Röhrich S, Heidinger BH, Prayer F, Milos RI, Apfaltrer P, Langs G, Baltzer PAT, Prosch H. Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging. Cancers (Basel) 2024; 16:1519. [PMID: 38672601 PMCID: PMC11049400 DOI: 10.3390/cancers16081519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. METHODS Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). RESULTS Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. CONCLUSIONS CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics.
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Affiliation(s)
- Martin L. Watzenboeck
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lucian Beer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Daria Kifjak
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Sebastian Röhrich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Benedikt H. Heidinger
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Florian Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Ruxandra-Iulia Milos
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Paul Apfaltrer
- Zentralröntgeninstitut für Diagnostik, Interventionelle Radiologie und Nuklearmedizin, Landesklinikum Wiener Neustadt, 2700 Wiener Neustadt, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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Hathaway QA, Abdeen Y, Conte J, Hass R, Santer MJ, Alyami B, Avalon JC, Patel B. Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024:10.1007/s10554-024-03101-2. [PMID: 38625628 DOI: 10.1007/s10554-024-03101-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.
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Affiliation(s)
- Quincy A Hathaway
- Department of Medical Education, West Virginia University, Morgantown, WV, USA
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Yahya Abdeen
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Justin Conte
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Rotem Hass
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Matthew J Santer
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Bandar Alyami
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Juan Carlo Avalon
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Brijesh Patel
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA.
- Department of Cardiovascular and Thoracic Surgery, West Virginia University School of Medicine, 1 Medical Center Drive, Morgantown, WV, 26505, USA.
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Levi R, Mollura M, Savini G, Garoli F, Battaglia M, Ammirabile A, Cappellini LA, Superbi S, Grimaldi M, Barbieri R, Politi LS. CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae. Sci Data 2024; 11:366. [PMID: 38605079 PMCID: PMC11009306 DOI: 10.1038/s41597-024-03191-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.
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Affiliation(s)
- Riccardo Levi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Maximiliano Mollura
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Federico Garoli
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Massimiliano Battaglia
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Angela Ammirabile
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Luca A Cappellini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Simona Superbi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Marco Grimaldi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Riccardo Barbieri
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Letterio S Politi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
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Fujii T, Iizawa Y, Kobayashi T, Hayasaki A, Ito T, Murata Y, Tanemura A, Ichikawa Y, Kuriyama N, Kishiwada M, Sakuma H, Mizuno S. Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy. Surg Today 2024:10.1007/s00595-024-02822-0. [PMID: 38581555 DOI: 10.1007/s00595-024-02822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 01/09/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD. METHODS The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors. RESULTS The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively. CONCLUSIONS Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.
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Affiliation(s)
- Takehiro Fujii
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
| | - Yusuke Iizawa
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Takumi Kobayashi
- School of Medicine, Faculty of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Aoi Hayasaki
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Takahiro Ito
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Yasuhiro Murata
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Akihiro Tanemura
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Naohisa Kuriyama
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Masashi Kishiwada
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Hajime Sakuma
- Department of Radiology, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Shugo Mizuno
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
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Hosseini SA, Shiri I, Ghaffarian P, Hajianfar G, Avval AH, Seyfi M, Servaes S, Rosa-Neto P, Zaidi H, Ay MR. The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods. Ann Nucl Med 2024:10.1007/s12149-024-01923-7. [PMID: 38575814 DOI: 10.1007/s12149-024-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). METHODS We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. RESULTS From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. CONCLUSION Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.
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Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Pardis Ghaffarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | | | - Milad Seyfi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
- University Research and Innovation Center, Óbudabuda University, Budapest, Hungary.
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
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Chen S, Gao Z, Qian Y, Chen Q. Key clinical predictors in the diagnosis of ovarian torsion in children. J Pediatr (Rio J) 2024:S0021-7557(24)00031-7. [PMID: 38582497 DOI: 10.1016/j.jped.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/13/2024] [Accepted: 01/22/2024] [Indexed: 04/08/2024] Open
Abstract
OBJECTIVE Ovarian torsion (OT) represents a severe gynecological emergency in female pediatric patients, necessitating immediate surgical intervention to prevent ovarian ischemia and preserve fertility. Prompt diagnosis is, therefore, paramount. This retrospective study set out to assess the utility of combined clinical, ultrasound, and laboratory features in diagnosing OT. METHODS The authors included 326 female pediatric patients aged under 14 years who underwent surgical confirmation of OT over a five-year period. Logistic regression analysis was employed to pinpoint factors linked with OT, and the authors compared clinical presentation, laboratory results, and ultrasound characteristics between patients with OT (OT group) and without OT (N-OT group). The authors conducted receiver operating characteristic (ROC) curve analysis to gauge the predictive capacity of the combined features. RESULTS Among 326, OT was confirmed in 24.23 % (79 cases) of the patients. The OT group had a higher incidence of prenatal ovarian masses than the N-OT (22 cases versus 7 cases) (p < 0.0001). Similarly, the authors observed significant differences in the presence of lower abdominal pain, suspected torsion on transabdominal ultrasound, and a high neutrophil-lymphocyte ratio (NLR > 3) between the OT and non-OT groups (p ˂ 0.05). Furthermore, when these parameters were combined, the resulting area under the curve (AUC) was 0.868, demonstrating their potential utility in OT diagnosis. CONCLUSION This study demonstrates a prediction model integrating clinical, laboratory, and ultrasound findings that can support the preoperative diagnosis of ovarian torsion, thereby enhancing diagnostic precision and improving patient management. Future prospective studies should concentrate on developing clinical predictive models for OT in pediatric patients.
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Affiliation(s)
- Sai Chen
- Zhejiang University School of Medicine, The Children's Hospital, National Clinical Research Center for Child Health, Department of Pediatric General Surgery, Zhejiang Province, China
| | - Zhigang Gao
- Zhejiang University School of Medicine, The Children's Hospital, National Clinical Research Center for Child Health, Department of Pediatric General Surgery, Zhejiang Province, China
| | - Yunzhong Qian
- Zhejiang University School of Medicine, The Children's Hospital, National Clinical Research Center for Child Health, Department of Pediatric General Surgery, Zhejiang Province, China
| | - Qingjiang Chen
- Zhejiang University School of Medicine, The Children's Hospital, National Clinical Research Center for Child Health, Department of Pediatric General Surgery, Zhejiang Province, China.
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Gu P, Chang JH, Carter D, McGovern DPB, Moore J, Wang P, Huang X. Radiomics-Based Analysis of Intestinal Ultrasound Images for Inflammatory Bowel Disease: A Feasibility Study. CROHN'S & COLITIS 360 2024; 6:otae034. [PMID: 38903657 PMCID: PMC11187771 DOI: 10.1093/crocol/otae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Indexed: 06/22/2024] Open
Abstract
Background The increasing adoption of intestinal ultrasound (IUS) for monitoring inflammatory bowel diseases (IBD) by IBD providers has uncovered new challenges regarding standardized image interpretation and limitations as a research tool. Artificial intelligence approaches can help address these challenges. We aim to determine the feasibility of radiomic analysis of IUS images and to determine if a radiomics-based classification model can accurately differentiate between normal and abnormal IUS images. We will also compare the radiomic-based model's performance to a convolutional neural network (CNN)-based classification model to understand which method is more effective for extracting meaningful information from IUS images. Methods Retrospectively analyzing IUS images obtained during routine outpatient visits, we developed and tested radiomic-based and CNN-based models to distinguish between normal and abnormal images, with abnormal images defined as bowel wall thickness > 3 mm or bowel hyperemia with modified Limberg score ≥ 1 (both are surrogate markers for inflammation). Model performances were measured by area under the receiver operator curve (AUC). Results For this feasibility study, 125 images (33% abnormal) were analyzed. A radiomic-based model using XG boost yielded the best classifier model with average test AUC 0.98%, 93.8% sensitivity, 93.8% specificity, and 93.7% accuracy. The CNN-based classification model yielded an average testing AUC of 0.75. Conclusions Radiomic analysis of IUS images is feasible, and a radiomic-based classification model could accurately differentiate abnormal from normal images. Our findings establish methods to facilitate future radiomic-based IUS studies that can help standardize image interpretation and expand IUS research capabilities.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jui-Hsuan Chang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Rozynek M, Tabor Z, Kłęk S, Wojciechowski W. Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study. Nutrition 2024; 120:112336. [PMID: 38237479 DOI: 10.1016/j.nut.2023.112336] [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: 08/14/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 02/24/2024]
Abstract
OBJECTIVES This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. METHODS The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms. RESULTS The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05). CONCLUSION Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.
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Affiliation(s)
- Miłosz Rozynek
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland
| | - Zbisław Tabor
- AGH University of Science and Technology, Krakow, Poland
| | - Stanisław Kłęk
- Surgical Oncology Clinic, Maria Skłodowska-Curie National Cancer Institute, Krakow, Poland
| | - Wadim Wojciechowski
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland.
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Borkar S, Chakole S, Prasad R, Bansod S. Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications. Cureus 2024; 16:e59203. [PMID: 38807819 PMCID: PMC11131437 DOI: 10.7759/cureus.59203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/14/2024] [Indexed: 05/30/2024] Open
Abstract
Digital health is poised to revolutionize the field of oncology, offering innovative solutions that enhance diagnostics, treatment, and patient care. This comprehensive review delves into the multifaceted landscape of digital health in oncology, encompassing its definition, significance, applications, benefits, challenges, ethical considerations, and future trends. Key findings highlight the potential for early detection, personalized treatment, enhanced care coordination, patient empowerment, accelerated research, and cost efficiency. Ethical concerns surrounding privacy, equitable access, and responsible data use are discussed. Looking ahead, the future of digital health in oncology is bright, driven by advancements in artificial intelligence, virtual and augmented reality, predictive analytics, global collaboration, and evolving regulations. This review underscores the need for collaboration among stakeholders and a patient-centered approach to harness the transformative power of digital health, promising a future where the burden of cancer is lessened through innovation and compassionate care.
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Affiliation(s)
- Samidha Borkar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Swarupa Chakole
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Roshan Prasad
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Spandan Bansod
- Obstetrics and Gynecological Nursing, Srimati Radhikabai Meghe Memorial College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Ferreira A, Li J, Pomykala KL, Kleesiek J, Alves V, Egger J. GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy. Med Image Anal 2024; 93:103100. [PMID: 38340545 DOI: 10.1016/j.media.2024.103100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.
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Affiliation(s)
- André Ferreira
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal; Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, 52074 Aachen, Germany.
| | - Jianning Li
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany.
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, Essen, 45147, Germany; TU Dortmund University, Department of Physics, Otto-Hahn-Straße 4, 44227 Dortmund, Germany.
| | - Victor Alves
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal.
| | - Jan Egger
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 801, Austria.
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Erdoğan MŞ, Arpak ES, Keles CSK, Villagra F, Işık EÖ, Afşar N, Yucesoy CA, Mur LAJ, Akanyeti O, Saybaşılı H. Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking. Eur J Neurosci 2024; 59:1789-1818. [PMID: 38221768 DOI: 10.1111/ejn.16245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/12/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024]
Abstract
Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.
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Affiliation(s)
| | - Esra Sümer Arpak
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - Cemre Su Kaya Keles
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
- Institute of Structural Mechanics and Dynamics in Aerospace Engineering, University of Stuttgart, Stuttgart, Germany
| | - Federico Villagra
- Department of Life Sciences, Aberystwyth University, Aberystwyth, Wales, UK
| | - Esin Öztürk Işık
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - Nazire Afşar
- Neurology, Acıbadem Mehmet Ali Aydınlar University, İstanbul, Turkey
| | - Can A Yucesoy
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - Luis A J Mur
- Department of Life Sciences, Aberystwyth University, Aberystwyth, Wales, UK
| | - Otar Akanyeti
- Department of Computer Science, Llandinam Building, Aberystwyth University, Aberystwyth, UK
| | - Hale Saybaşılı
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
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Alan N, Zenkin S, Lavadi RS, Legarreta AD, Hudson JS, Fields DP, Agarwal N, Mamindla P, Ak M, Peddagangireddy V, Puccio L, Buell TJ, Hamilton DK, Kanter AS, Okonkwo DO, Zinn PO, Colen RR. Associating T1-Weighted and T2-Weighted Magnetic Resonance Imaging Radiomic Signatures With Preoperative Symptom Severity in Patients With Cervical Spondylotic Myelopathy. World Neurosurg 2024; 184:e137-e143. [PMID: 38253177 DOI: 10.1016/j.wneu.2024.01.072] [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: 11/18/2023] [Accepted: 01/14/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND Preoperative symptom severity in cervical spondylotic myelopathy (CSM) can be variable. Radiomic signatures could provide an imaging biomarker for symptom severity in CSM. This study utilizes radiomic signatures of T1-weighted and T2-weighted magnetic resonance imaging images to correlate with preoperative symptom severity based on modified Japanese Orthopaedic Association (mJOA) scores for patients with CSM. METHODS Sixty-two patients with CSM were identified. Preoperative T1-weighted and T2-weighted magnetic resonance imaging images for each patient were segmented from C2-C7. A total of 205 texture features were extracted from each volume of interest. After feature normalization, each second-order feature was further subdivided to yield a total of 400 features from each volume of interest for analysis. Supervised machine learning was used to build radiomic models. RESULTS The patient cohort had a median mJOA preoperative score of 13; of which, 30 patients had a score of >13 (low severity) and 32 patients had a score of ≤13 (high severity). Radiomic analysis of T2-weighted imaging resulted in 4 radiomic signatures that correlated with preoperative mJOA with a sensitivity, specificity, and accuracy of 78%, 89%, and 83%, respectively (P < 0.004). The area under the curve value for the ROC curves were 0.69, 0.70, and 0.77 for models generated by independent T1 texture features, T1 and T2 texture features in combination, and independent T2 texture features, respectively. CONCLUSIONS Radiomic models correlate with preoperative mJOA scores using T2 texture features in patients with CSM. This may serve as a surrogate, objective imaging biomarker to measure the preoperative functional status of patients.
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Affiliation(s)
- Nima Alan
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California.
| | - Serafettin Zenkin
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Raj Swaroop Lavadi
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Andrew D Legarreta
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Joseph S Hudson
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Daryl P Fields
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Nitin Agarwal
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Priyadarshini Mamindla
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Murat Ak
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Vishal Peddagangireddy
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Lauren Puccio
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Thomas J Buell
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - D Kojo Hamilton
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Adam S Kanter
- Department of Neurosurgery, Hoag Neurosciences Institute, Newport Beach, California
| | - David O Okonkwo
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Pascal O Zinn
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rivka R Colen
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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50
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Zhang K, Abdoli N, Gilley P, Sadri Y, Chen X, Thai TC, Dockery L, Moore K, Mannel RS, Qiu Y. Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients. Comput Biol Med 2024; 172:108240. [PMID: 38460312 DOI: 10.1016/j.compbiomed.2024.108240] [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: 08/18/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.
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Affiliation(s)
- Ke Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA, 73019; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Patrik Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Youkabed Sadri
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Theresa C Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Lauren Dockery
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Kathleen Moore
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Robert S Mannel
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Yuchen Qiu
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA, 73019; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019.
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