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Tan HQ, Cai J, Tay SH, Sim AY, Huang L, Chua ML, Tang Y. Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis. Comput Struct Biotechnol J 2024; 23:43-51. [PMID: 38125298 PMCID: PMC10730953 DOI: 10.1016/j.csbj.2023.11.040] [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: 08/02/2023] [Revised: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Background Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab. Purpose We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab. Methods 118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features. Results 71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation). Conclusion Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.
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Affiliation(s)
- Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Jinhua Cai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Shi Hui Tay
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Adelene Y.L. Sim
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Luo Huang
- Department of Radiation Oncology, Chongqing University Cancer Hospital, People's Republic of China
| | - Melvin L.K. Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
- Oncology Academic Programme, Duke-NUS Medical School, Singapore
| | - Yamei Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
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Luo HJ, Ren JL, Mei Guo L, Liang Niu J, Song XL. MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma. Eur J Radiol Open 2024; 13:100592. [PMID: 39149534 PMCID: PMC11324846 DOI: 10.1016/j.ejro.2024.100592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/08/2024] [Accepted: 07/14/2024] [Indexed: 08/17/2024] Open
Abstract
Background Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC). Objective This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC. Methods A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC). Results In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively. Conclusions Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.
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Affiliation(s)
- Hong-Jian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zuiyi, Guizhou province, China
| | | | - Li Mei Guo
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Jin Liang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Xiao-Li Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
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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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Joye AA, Bogowicz M, Gote-Schniering J, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S, Gabryś HS. Radiomics on slice-reduced versus full-chest computed tomography for diagnosis and staging of interstitial lung disease in systemic sclerosis: A comparative analysis. Eur J Radiol Open 2024; 13:100596. [PMID: 39280121 PMCID: PMC11402420 DOI: 10.1016/j.ejro.2024.100596] [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: 06/05/2024] [Revised: 07/26/2024] [Accepted: 08/13/2024] [Indexed: 09/18/2024] Open
Abstract
Purpose The purpose of this study was to evaluate the efficacy of radiomics derived from slice-reduced CT (srCT) scans versus full-chest CT (fcCT) for diagnosing and staging of interstitial lung disease (ILD) in systemic sclerosis (SSc), considering the potential to reduce radiation exposure. Material and methods The fcCT corresponded to a standard high-resolution full-chest CT whereas the srCT consisted of nine axial slices. 1451 radiomic features in two dimensions from srCT and 1375 features in three dimensions from fcCT scans were extracted from 166 SSc patients. The study included first- and second-order features from original and wavelet-transformed images. We assessed the predictive performance of quantitative CT (qCT)-based logistic regression (LR) models relying on preselected features and machine learning workflows involving LR and extra-trees classifiers with data-driven feature selection. The area under the receiver operating characteristic curve (AUC) was used to estimate model performance. Results The best models for diagnosis and staging ILD achieved AUC=0.85±0.08 and AUC=0.82±0.08 with srCT, and AUC=0.83±0.06 and AUC=0.76±0.08 with fcCT, respectively. srCT-based models showed slightly superior performance over fcCT-based models, particularly in 2D-radiomic analyses when interpolation resolution closely matched the original in-plane resolution. For diagnosis, the LR outperformed qCT-models, whereas for staging, the best results were obtained with a qCT-based model. Conclusions Radiomics from srCT is an effective and preferable alternative to fcCT for diagnosing and staging SSc-ILD. This approach not only enhances predictive accuracy but also minimizes radiation exposure risks, offering a promising avenue for improved treatment decision support in SSc-ILD management.
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Affiliation(s)
- Anja A Joye
- University Hospital of Zurich, Department of Radiation Oncology, Rämistrasse 100, Zürich 8091, Switzerland
| | - Marta Bogowicz
- University Hospital of Zurich, Department of Radiation Oncology, Rämistrasse 100, Zürich 8091, Switzerland
| | - Janine Gote-Schniering
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- University Hospital of Zurich, University Zurich, Institute for Diagnostic and Interventional Radiology, Switzerland
| | - Matthias Guckenberger
- University Hospital of Zurich, Department of Radiation Oncology, Rämistrasse 100, Zürich 8091, Switzerland
| | - Britta Maurer
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- University Hospital of Zurich, Department of Radiation Oncology, Rämistrasse 100, Zürich 8091, Switzerland
| | - Hubert S Gabryś
- University Hospital of Zurich, Department of Radiation Oncology, Rämistrasse 100, Zürich 8091, Switzerland
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Ferro A, Bottosso M, Dieci MV, Scagliori E, Miglietta F, Aldegheri V, Bonanno L, Caumo F, Guarneri V, Griguolo G, Pasello G. Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives. Crit Rev Oncol Hematol 2024; 203:104479. [PMID: 39151838 DOI: 10.1016/j.critrevonc.2024.104479] [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: 01/10/2024] [Revised: 07/22/2024] [Accepted: 08/10/2024] [Indexed: 08/19/2024] Open
Abstract
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.
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Affiliation(s)
- Alessandra Ferro
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Michele Bottosso
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Maria Vittoria Dieci
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy.
| | - Elena Scagliori
- Radiology Unit, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Federica Miglietta
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Vittoria Aldegheri
- Radiology Unit, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Laura Bonanno
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Francesca Caumo
- Unit of Breast Radiology, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Valentina Guarneri
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Gaia Griguolo
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Giulia Pasello
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
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Yuan J, Wu M, Qiu L, Xu W, Fei Y, Zhu Y, Shi K, Li Y, Luo J, Ding Z, Sun X, Zhou S. Tumor habitat-based MRI features assessing early response in locally advanced nasopharyngeal carcinoma. Oral Oncol 2024; 158:106980. [PMID: 39151333 DOI: 10.1016/j.oraloncology.2024.106980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/08/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024]
Abstract
OBJECTIVE The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors. METHODS A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833). CONCLUSION The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.
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Affiliation(s)
- Jinling Yuan
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Mengxing Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lei Qiu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Weilin Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yinjiao Fei
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yuchen Zhu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Kexin Shi
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Yurong Li
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Jinyan Luo
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Zhou Ding
- Department of Radiation Oncology, Lianshui County People's Hospital, Huai'an 223400, Jiangsu, China.
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| | - Shu Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
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Ahmed TM, Zhu Z, Yasrab M, Blanco A, Kawamoto S, He J, Fishman EK, Chu L, Javed AA. Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model. Ann Surg Oncol 2024; 31:8136-8145. [PMID: 39179862 DOI: 10.1245/s10434-024-16064-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 08/04/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs). PATIENTS AND METHODS An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally. RESULTS Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Zhuotun Zhu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Mohammad Yasrab
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Alejandra Blanco
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jin He
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Ammar A Javed
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, NYU Langone Grossman School of Medicine, New York, NY, USA.
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Zhang J, Wang Q, Guo TH, Gao W, Yu YM, Wang RF, Yu HL, Chen JJ, Sun LL, Zhang BY, Wang HJ. Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer. World J Gastrointest Oncol 2024; 16:4115-4128. [DOI: 10.4251/wjgo.v16.i10.4115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 08/18/2024] [Accepted: 08/28/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.
AIM To establish a radiomic model to predict the response of AGC patients to nICT.
METHODS Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test.
RESULTS The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.
CONCLUSION A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.
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Affiliation(s)
- Jun Zhang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Qi Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Tian-Hui Guo
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Wen Gao
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Yi-Miao Yu
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Rui-Feng Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Hua-Long Yu
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Jing-Jing Chen
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Ling-Ling Sun
- Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Bi-Yuan Zhang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Hai-Ji Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
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Lin A, Zhang H, Wang Y, Cui Q, Zhu K, Zhou D, Han S, Meng S, Han J, Li L, Zhou C, Ma X. Radiomics based on MRI to predict recurrent L4-5 disc herniation after percutaneous endoscopic lumbar discectomy. BMC Med Imaging 2024; 24:273. [PMID: 39390384 DOI: 10.1186/s12880-024-01450-x] [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: 10/17/2023] [Accepted: 10/01/2024] [Indexed: 10/12/2024] Open
Abstract
BACKGROUND In recent years, radiomics has been shown to be an effective tool for the diagnosis and prediction of diseases. Existing evidence suggests that imaging features play a key role in predicting the recurrence of lumbar disk herniation (rLDH). Thus, this study aimed to evaluate the risk of rLDH in patients undergoing percutaneous endoscopic lumbar discectomy (PELD) using radiomics to facilitate the development of more rational surgical and perioperative management strategies. METHOD This was a retrospective case-control study involving 487 patients who underwent PELD at the L4/5 level. The rLDH and negative groups were matched using propensity score matching (PSM). A total of 1409 radiomic features were extracted from preoperative lumbar MRI images using intraclass correlation coefficient (ICC) analysis, t-test, and LASSO analysis. Afterward, 6 predictive models were constructed and evaluated using ROC curve analysis, AUC, specificity, sensitivity, confusion matrix, and 2 repeated 3-fold cross-validations. Lastly, the Shapley Additive Explanation (SHAP) analysis provided visual explanations for the models. RESULTS Following screening and matching, 128 patients were included in both the recurrence and control groups. Moreover, 18 of the extracted radiomic features were selected for generating six models, which achieved an AUC of 0.551-0.859 for predicting rLDH. Among these models, SVM, RF, and XG Boost exhibited superior performances. Finally, cross-validation revealed that their accuracy was 0.674-0.791, 0.647-0.729, and 0.674-0.718. CONCLUSION Radiomics based on MRI can be used to predict the risk of rLDH, offering more comprehensive guidance for perioperative treatment by extracting imaging information that cannot be visualized with the naked eye. Meanwhile, the accuracy and generalizability of the model can be improved in the future by incorporating more data and conducting multicenter studies.
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Affiliation(s)
- Antao Lin
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Yan Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Qian Cui
- Department of Medical Imaging, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Kai Zhu
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Dan Zhou
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Shuo Han
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Shengwei Meng
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Jialuo Han
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Lei Li
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China
| | - Chuanli Zhou
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China.
| | - Xuexiao Ma
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, Shandong, 266000, People's Republic of China.
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10
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van Tuijl RJ, den Hertog CS, Timmins KM, Velthuis BK, van Ooij P, Zwanenburg JJM, Ruigrok YM, van der Schaaf IC. Intra-Aneurysmal High-Resolution 4D MR Flow Imaging for Hemodynamic Imaging Markers in Intracranial Aneurysm Instability. AJNR Am J Neuroradiol 2024:ajnr.A8380. [PMID: 38991775 DOI: 10.3174/ajnr.a8380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/04/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND AND PURPOSE Prediction of aneurysm instability is crucial to guide treatment decisions and to select appropriate patients with unruptured intracranial aneurysms (IAs) for preventive treatment. High-resolution 4D MR flow imaging and 3D quantification of aneurysm morphology could offer insights and new imaging markers for aneurysm instability. In this cross-sectional study, we aim to identify 4D MR flow imaging markers for aneurysm instability by relating hemodynamics in the aneurysm sac to 3D morphologic proxy parameters for aneurysm instability. MATERIALS AND METHODS In 35 patients with 37 unruptured IAs, a 3T MRA and a 7T 4D MRI flow scan were performed. Five hemodynamic parameters-peak-systolic wall shear stress (WSSMAX) and time-averaged wall shear stress (WSSMEAN), oscillatory shear index (OSI), mean velocity, and velocity pulsatility index-were correlated to 6 3D morphology proxy parameters of aneurysm instability-major axis length, volume, surface area (all 3 size parameters), flatness, shape index, and curvedness-by Pearson correlation with 95% CI. Scatterplots of hemodynamic parameters that correlated with IA size (major axis length) were created. RESULTS WSSMAX and WSSMEAN correlated negatively with all 3 size parameters (strongest for WSSMEAN with volume (r = -0.70, 95% CI -0.83 to -0.49) and OSI positively (strongest with major axis length [r = 0.87, 95% CI 0.76-0.93]). WSSMAX and WSSMEAN correlated positively with shape index (r = 0.61, 95% CI 0.36-0.78 and r = 0.49, 95% CI 0.20-0.70, respectively) and OSI negatively (r = -0.82, 95% CI -0.9 to -0.68). WSSMEAN and mean velocity correlated negatively with flatness (r = -0.35, 95% CI -0.61 to -0.029 and r = -0.33, 95% CI -0.59 to 0.007, respectively) and OSI positively (r = 0.54, 95% CI 0.26-0.74). Velocity pulsatility index did not show any statistically relevant correlation. CONCLUSIONS Out of the 5 included hemodynamic parameters, WSSMAX, WSSMEAN, and OSI showed the strongest correlation with morphologic 3D proxy parameters of aneurysm instability. Future studies should assess these promising new imaging marker parameters for predicting aneurysm instability in longitudinal cohorts of patients with IA.
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Affiliation(s)
- R J van Tuijl
- From the Department of Radiology (R.J.v.T., K.M.T., B.K.V., I.C.v.d.S.), University Medical Center Utrecht, Utrecht, the Netherlands
- Translational Neuroimaging Group, Center for Image Sciences (R.J.v.T., J.J.M.Z.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - C S den Hertog
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center (C.S.d.H., Y.M.R.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - K M Timmins
- From the Department of Radiology (R.J.v.T., K.M.T., B.K.V., I.C.v.d.S.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - B K Velthuis
- From the Department of Radiology (R.J.v.T., K.M.T., B.K.V., I.C.v.d.S.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - P van Ooij
- Department of Radiology & Nuclear Medicine (P.v.O.), Amsterdam University Medical Center location AMC, Amsterdam, the Netherlands
- Department of Pediatric Cardiology (P.v.O.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - J J M Zwanenburg
- Translational Neuroimaging Group, Center for Image Sciences (R.J.v.T., J.J.M.Z.), University Medical Center Utrecht, Utrecht, the Netherlands
| | - Y M Ruigrok
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center (C.S.d.H., Y.M.R.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - I C van der Schaaf
- From the Department of Radiology (R.J.v.T., K.M.T., B.K.V., I.C.v.d.S.), University Medical Center Utrecht, Utrecht, the Netherlands
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11
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Ammari S, Quillent A, Elvira V, Bidault F, Garcia GCTE, Hartl DM, Balleyguier C, Lassau N, Chouzenoux É. Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01255-y. [PMID: 39390287 DOI: 10.1007/s10278-024-01255-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 10/12/2024]
Abstract
The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use.
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Affiliation(s)
- Samy Ammari
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Arnaud Quillent
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Víctor Elvira
- School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - François Bidault
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Gabriel C T E Garcia
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Dana M Hartl
- Department of Otolaryngology Head and Neck Surgery, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Corinne Balleyguier
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Nathalie Lassau
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France
| | - Émilie Chouzenoux
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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12
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Iacoban CG, Ramaglia A, Severino M, Tortora D, Resaz M, Parodi C, Piccardo A, Rossi A. Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art. Neuroradiology 2024:10.1007/s00234-024-03476-y. [PMID: 39382639 DOI: 10.1007/s00234-024-03476-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 09/30/2024] [Indexed: 10/10/2024]
Abstract
In the pediatric age group, brain neoplasms are the second most common tumor category after leukemia, with an annual incidence of 6.13 per 100,000. Conventional MRI sequences, complemented by CT whenever necessary, are fundamental for the initial diagnosis and surgical planning as well as for post-operative evaluations, assessment of response to treatment, and surveillance; however, they have limitations, especially concerning histopathologic or biomolecular phenotyping and grading. In recent years, several advanced MRI sequences, including diffusion-weighted imaging, diffusion tensor imaging, arterial spin labelling (ASL) perfusion, and MR spectroscopy, have emerged as a powerful aid to diagnosis as well as prognostication; furthermore, other techniques such as diffusion kurtosis, amide proton transfer imaging, and MR elastography are being translated from the research environment to clinical practice. Molecular imaging, especially PET with amino-acid tracers, complement MRI in several aspects, including biopsy targeting and outcome prediction. Finally, radiomics with radiogenomics are opening entirely new perspectives for a quantitative approach aiming at identifying biomarkers that can be used for personalized, precision management strategies.
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Affiliation(s)
| | - Antonia Ramaglia
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy
| | - Mariasavina Severino
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy
| | - Martina Resaz
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy
| | - Costanza Parodi
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy
| | - Arnoldo Piccardo
- Department of Nuclear Medicine, E.O. Ospedali Galliera, Genoa, Italy
| | - Andrea Rossi
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genoa, Italy.
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
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13
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Jiang H, Liu A, Ying Z. Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models. Sci Rep 2024; 14:23525. [PMID: 39384824 PMCID: PMC11464731 DOI: 10.1038/s41598-024-74418-0] [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: 01/02/2024] [Accepted: 09/25/2024] [Indexed: 10/11/2024] Open
Abstract
To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of 54 patients with CP and 71 healthy controls was randomly separated into training and validation groups, using a 7:3 ratio. Radiomics features were extracted from grey-matter and white-matter in the filtered mwp0* image. The Mann-Whitney U test, Spearman's rank correlation test, and least absolute shrinkage and selection operator (LASSO) were utilized to select features. An XGBoost model was created based on these features, and Shapley Additive exPlanations (SHAP) was used for personalization and visual interpretation. A nomogram was developed for the diagnosis of FMS subgroups, utilizing radiomics scores and clinical predictors. The efficacy of the nomogram was evaluated using the area under the receiver operating characteristic curve, while decision curve analysis was employed to evaluate its clinical efficacy. The XGBoost model displays stability in the training validation group, indicating lower overfitting of CP model. The nomogram model combined with the rad-score has a greater ability to distinguish between typical and sub-clinical than the clinical factor model alone. We developed and validated a CP diagnosis model by XGBoost and realized model visualization through SHAP. The rad-score obtained by machine learning was used to build a nomogram model that combines clinical scales to distinguish patients with typical and sub-clinical fibromyalgia.
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Affiliation(s)
- Hongyang Jiang
- Medical College of Soochow University, Suzhou, China
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Aihui Liu
- Center for General Practice Medicine, Department of Rheumatology and Immunology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenhua Ying
- Medical College of Soochow University, Suzhou, China.
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Luo L, Wang X, Xie H, Liang H, Gao J, Li Y, Xia Y, Zhao M, Shi F, Shen C, Duan X. Role of [ 18F]-PSMA-1007 PET radiomics for seminal vesicle invasion prediction in primary prostate cancer. Comput Biol Med 2024; 183:109249. [PMID: 39388841 DOI: 10.1016/j.compbiomed.2024.109249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 09/23/2024] [Accepted: 10/03/2024] [Indexed: 10/12/2024]
Abstract
PURPOSE The purpose of this study is to investigate the diagnostic utility of [18F]-PSMA-1007 PET radiomics combined with machine learning methods to predict seminal vesicle invasion (SVI) after radical prostatectomy (RP) in prostate cancer (PCa) patients. METHODS This is a post hoc retrospective analysis for a prospective clinical trial that included a consecutive sample of PCa patients (n = 140) who had [18F]-PSMA-1007 PET/CT prior to RP. The intraprostatic lesion's volume of interest (VOI) was semi-automatically sketched using a threshold of 40 % maximum standardized uptake value (SUVmax), namely 40%SUVmax-VOI, and seminal vesicle glands were manually contoured, namely SV-VOI. Models were built using a variety of machine learning methods such as logistic regression, random forest, and support vector machine. The area under the receiver operating characteristic curve (AUC) was calculated for different models, and the prediction performances of radiomics models were compared against the radiologists' assessment. Kaplan-Meier analysis was utilized to assess the effectiveness of selected radiomics features to determine the progression-free survival (PFS) probability. RESULTS The training set had 112 patients and the test set had 28 patients. The highest AUC for the PET radiomics model of 40%SUVmax-VOI and the PET radiomics model of SV-VOI were 0.85 and 0.96 in the test set, respectively. The PET radiomics model of SV-VOI had a significantly higher AUC compared to the radiologists' assessment (P < 0.05). The Kaplan-Meier analysis showed that PET radiomics features were associated with PFS in patients with PCa. CONCLUSION Radiomics models developed by preoperative [18F]-PSMA-1007 PET were proven useful in predicting SVI, and PSMA PET radiomics features were correlated with PFS, suggesting that the PSMA PET radiomics might be an accurate tool for PCa characterization.
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Affiliation(s)
- Liang Luo
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinyi Wang
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongjun Xie
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hua Liang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jungang Gao
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yang Li
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Mengmeng Zhao
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Cong Shen
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyi Duan
- PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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15
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Lin J, Su CQ, Tang WT, Xia ZW, Lu SS, Hong XN. Radiomic features on multiparametric MRI for differentiating pseudoprogression from recurrence in high-grade gliomas. Acta Radiol 2024:2841851241283781. [PMID: 39380365 DOI: 10.1177/02841851241283781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
BACKGROUND Distinguishing between tumor recurrence and pseudoprogression (PsP) in high-grade glioma postoperatively is challenging. This study aims to enhance this differentiation using a combination of intratumoral and peritumoral radiomics. PURPOSE To assess the effectiveness of intratumoral and peritumoral radiomics in improving the differentiation between high-grade glioma recurrence and pseudoprogression after surgery. MATERIAL AND METHODS A total of 109 cases were randomly divided into training and validation sets, with 1316 features extracted from intratumoral and peritumoral volumes of interest (VOIs) on conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Feature selection was performed using the mRMR algorithm, resulting in intratumoral (100 features), peritumoral (100 features), and combined (200 features) subsets. Optimal features were then selected using PCC and RFE algorithms and modeled using LR, SVM, and LDA classifiers. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC), evaluated in the validation set. A nomogram was established using radscores from intratumoral, peritumoral, and combined models. RESULTS The combined model, utilizing 14 optimal features (8 peritumoral, 6 intratumoral) and LR as the best classifier, outperformed the single intratumoral and peritumoral models. In the training set, the AUC values for the combined model, intratumoral model, and peritumoral model were 0.938, 0.921, and 0.847, respectively; in the validation set, the AUC values were 0.841, 0.755, and 0.705. The nomogram model demonstrated AUCs of 0.960 (training set) and 0.850 (validation set). CONCLUSION The combination of intratumoral and peritumoral radiomics is effective in distinguishing high-grade glioma recurrence from pseudoprogression after surgery.
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Affiliation(s)
- Jie Lin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Chun-Qiu Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Wen-Tian Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Zhi-Wei Xia
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Xun-Ning Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
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Singh G, Singh A, Bae J, Manjila S, Spektor V, Prasanna P, Lignelli A. -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates. Cancer Imaging 2024; 24:133. [PMID: 39375809 PMCID: PMC11460168 DOI: 10.1186/s40644-024-00769-6] [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: 07/28/2024] [Accepted: 08/31/2024] [Indexed: 10/09/2024] Open
Abstract
Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/-10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.
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Affiliation(s)
- Gagandeep Singh
- Neuroradiology Division, Columbia University Irving Medical Center, New York, NY, USA.
| | - Annie Singh
- Atal Bihari Vajpayee Institute of Medical Sciences, New Delhi, India
| | - Joseph Bae
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA
| | - Sunil Manjila
- Department of Neurological Surgery, Garden City Hospital, Garden City, MI, USA
| | - Vadim Spektor
- Neuroradiology Division, Columbia University Irving Medical Center, New York, NY, USA
| | - Prateek Prasanna
- Department of Neurological Surgery, Garden City Hospital, Garden City, MI, USA
| | - Angela Lignelli
- Neuroradiology Division, Columbia University Irving Medical Center, New York, NY, USA
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Gong J, Wang Q, Li J, Yang Z, Zhang J, Teng X, Sun H, Cai J, Zhao L. Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy. Insights Imaging 2024; 15:239. [PMID: 39373828 PMCID: PMC11458848 DOI: 10.1186/s13244-024-01816-3] [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: 08/05/2024] [Accepted: 09/10/2024] [Indexed: 10/08/2024] Open
Abstract
OBJECTIVES Repeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT). METHODS Nine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS. RESULTS First-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42-0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70-0.84 vs 0.14-0.64). Features with smaller bin widths had higher repeatability (median ICC of 8-128: 0.65-0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013). CONCLUSIONS Applying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC. CRITICAL RELEVANCE STATEMENT The exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research. KEY POINTS The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.
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Affiliation(s)
- Jie Gong
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Qifeng Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhi Yang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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Wang Q. Refining Radiomics by Integrating Vascular Information from Color Doppler Ultrasound for Assessing Lymph Node Metastasis in Endometrial Cancer. Acad Radiol 2024:S1076-6332(24)00713-X. [PMID: 39379239 DOI: 10.1016/j.acra.2024.09.064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Accepted: 09/28/2024] [Indexed: 10/10/2024]
Affiliation(s)
- QiongJun Wang
- Department of Ultrasound, Huizhou Central People's Hospital, Huizhou 516000, China (Q.W.).
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19
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Zhang Y, Huang W, Jiao H, Kang L. PET radiomics in lung cancer: advances and translational challenges. EJNMMI Phys 2024; 11:81. [PMID: 39361110 PMCID: PMC11450131 DOI: 10.1186/s40658-024-00685-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 09/26/2024] [Indexed: 10/06/2024] Open
Abstract
Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.
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Affiliation(s)
- Yongbai Zhang
- Department of Nuclear Medicine, Peking University First Hospital, No. 8 Xishiku Str., Xicheng Dist, Beijing, 100034, China
| | - Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, No. 8 Xishiku Str., Xicheng Dist, Beijing, 100034, China
| | - Hao Jiao
- Department of Nuclear Medicine, Peking University First Hospital, No. 8 Xishiku Str., Xicheng Dist, Beijing, 100034, China
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, No. 8 Xishiku Str., Xicheng Dist, Beijing, 100034, China.
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20
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Stern NM, Mikalsen LTG, Dueland S, Schulz A, Line PD, Stokke C, Grut H. The prognostic value of [ 18F]FDG PET/CT texture analysis prior to transplantation for unresectable colorectal liver metastases. Clin Physiol Funct Imaging 2024. [PMID: 39358976 DOI: 10.1111/cpf.12908] [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: 01/05/2024] [Revised: 08/21/2024] [Accepted: 09/18/2024] [Indexed: 10/04/2024]
Abstract
INTRODUCTION To determine whether heterogeneity in colorectal liver metastases (CRLM) 18F fluorodeoxyglucose [18F]FDG distribution is predictive of disease-free survival (DFS) and overall survival (OS) following liver transplantation (LT) for unresectable CRLM. METHODS The preoperative [18F]FDG positron emission tomography/computed tomography examinations of all patients in the secondary cancer 1 and 2 studies were retrospectively assessed. Maximum standardized uptake value (SUVmax), metabolic tumour volume (MTV), and six texture heterogeneity parameters (joint entropyGLCM, dissimilarityGLCM, grey level varianceSZM, size zone varianceSZM, and zone percentageSZM, and morphological feature convex deficiency) were obtained. DFS and OS for patients over and under the median value for each of these parameters were compared by using the Kaplan Meier method and log rank test. RESULTS Twenty-eight out of 40 patients who underwent LT for unresectable CRLM had liver metastases with uptake above liver background and were eligible for inclusion. Low MTV (p < 0.001) and dissimilarityGLCM (p = 0.016) were correlated to longer DFS. Low MTV (p < 0.001) and low values of the texture parameters dissimilarityGLCM (p = 0.038), joint entropyGLCM (p = 0.015) and zone percentageSZM (p = 0.037) were significantly correlated to longer OS. SUVmax was not correlated to DFS and OS. CONCLUSION Although some texture parameters were able to significantly predict DFS and OS, MTV seems to be superior to predict both DFS and OS following LT for unresectable CRLM.
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Affiliation(s)
- Nadide Mutlukoca Stern
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Svein Dueland
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - A Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pål-Dag Line
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Caroline Stokke
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Harald Grut
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
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21
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Yu S, Yang Y, Wang Z, Zheng H, Cui J, Zhan Y, Liu J, Li P, Fan Y, Jia W, Wang M, Chen B, Tao J, Li Y, Zhang X. CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions. Cancer Imaging 2024; 24:130. [PMID: 39358821 PMCID: PMC11446113 DOI: 10.1186/s40644-024-00775-8] [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: 11/10/2023] [Accepted: 09/16/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions. METHODS CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation. RESULTS Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively. CONCLUSIONS The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.
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Affiliation(s)
- Shuanbao Yu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Yang
- Department of Information Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zeyuan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haoke Zheng
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinshan Cui
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yonghao Zhan
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junxiao Liu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peng Li
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yafeng Fan
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wendong Jia
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Chen
- Department of Urology, Tongliao Clinical College, Inner Mongolia Medical University, Tongliao, China
| | - Jin Tao
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuhong Li
- Department of Information Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuepei Zhang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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22
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Li J, Cui N, Wang Y, Li W, Jiang Z, Liu W, Guo C, Wang K. Prediction of preoperative lymph-vascular space invasion and survival outcomes of cervical squamous cell carcinoma by utilizing 18F-FDG PET/CT imaging at early stage. Nucl Med Commun 2024:00006231-990000000-00344. [PMID: 39354802 DOI: 10.1097/mnm.0000000000001909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
OBJECTIVE To establish nomograms for predicting preoperative lymph-vascular space invasion (LVSI) and survival outcomes of cervical squamous cell carcinoma (CSCC) based on PET/CT radiomics. METHODS One hundred and twenty-three patients with CSCC and LVSI status were enrolled retrospectively. Independent predictors of LVSI were identified through clinicopathological factors and PET/CT metabolic parameters. We extracted 1316 features from PET and CT volume of interest, respectively. Additionally, four models (PET-RS: radiomic signature of PET only; CT-RS: radiomic signature of CT only; PET/CT-RS + clinical data; PET/CT-RS: radiomic signature of PET and CT) were established to predict LVSI status. Calculation of radiomics scores of PET/CT was executed for assessment of the survival outcomes, followed by development of nomograms with radiomics (NR) or without radiomics (NWR). RESULTS One hundred and twenty-three patients with pathologically confirmed CSCC had been categorized into two sets (training and testing sets). It was found that only maximum standardized uptake value (SUVmax) and squamous cell carcinoma antigen were independent predictors of LVSI. Meanwhile, the PET/CT-RS + clinical data outperformed the other three models in the training set [area under the curve (AUC): 0.91 vs. 0.861 vs. 0.81 vs. 0.814] and the testing set (AUC: 0.885 vs. 0.857 vs. 0.783 vs. 0.798). Additionally, SUVmax and LVSI had been demonstrated to be independent prognostic indicators for progression-free survival and overall survival. Decision curve analysis and calibration curve indicated that NRs were superior to NWRs. The survival outcomes were assessed. CONCLUSION PET/CT-based radiomic signature nomogram enables a new method for preoperative prediction of LVSI and survival prognosis for patients with CSCC.
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Affiliation(s)
- Jiatong Li
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province
| | - Nan Cui
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province
| | - Yanmei Wang
- Scientific Research Center Department, Beijing General Electric Company, Beijing
| | - Wei Li
- Interventional Vascular Surgery Department, Harbin Medical University, The 4th Affiliated Hospital of Harbin Medical University
| | - Zhiyun Jiang
- Departments of Radiology, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province
| | - Chenxu Guo
- Pathology, Harbin Medical University, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province, China
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province
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23
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Le TK, Comte V, Darcourt J, Razzouk-Cadet M, Rollet AC, Orlhac F, Humbert O. Performance and Clinical Impact of Radiomics and 3D-CNN Models for the Diagnosis of Neurodegenerative Parkinsonian Syndromes on 18 F-FDOPA PET. Clin Nucl Med 2024; 49:924-930. [PMID: 39104036 DOI: 10.1097/rlu.0000000000005392] [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: 08/07/2024]
Abstract
PURPOSE The aim of this study was to compare the performance and added clinical value of a semiautomated radiomics model and an automated 3-dimensinal convolutional neural network (3D-CNN) model for diagnosing neurodegenerative parkinsonian syndromes on 18 F-FDOPA PET images. PATIENTS AND METHODS This 2-center retrospective study included 687 patients with motor symptoms consistent with parkinsonian syndrome. All patients underwent 18 F-FDOPA brain PET scans, acquired on 3 PET systems from 2 different hospitals, and classified as pathological or nonpathological (by an expert nuclear physician). Artificial intelligence models were trained to replicate this medical expert's classification using 2 pipelines. The radiomics pipeline was semiautomated and involved manually segmenting the bilateral caudate and putamen nuclei; 43 radiomic features were extracted and combined using the support vector machine method. The deep learning pipeline was fully automatic and used a 3D-CNN model. Both models were trained on 417 patients and tested on an internal (n = 100) and an external (n = 170) test set. The final models' performance was evaluated using balanced accuracy and compared with that of a junior medical expert and nonexpert nuclear physician. RESULTS On the internal test set, the 3D-CNN model outperformed the radiomic model with a balanced accuracy of 99% (vs 96%). It led to diagnostic performance similar to that of a junior medical expert (only 1 in 100 patients misclassified by both). On the external test set from a less experienced hospital, the 3D-CNN model allowed physicians to correctly reclassify the diagnosis of 10 out 170 patients (6%). CONCLUSIONS The developed 3D-CNN model can automatically diagnose neurodegenerative parkinsonian syndromes, also reducing diagnostic errors by 6% in less-experienced hospitals.
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Affiliation(s)
- Thi Khuyen Le
- From the Université Côte D'Azur, CNRS, Inserm, iBV, Nice, France
| | | | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, UCA, Nice, France
| | | | | | - Fanny Orlhac
- Laboratory of Translational Imaging in Oncology (LITO-U1288), Curie Institute, Inserm, PSL University, Orsay, France
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Salimi Y, Hajianfar G, Mansouri Z, Sanaat A, Amini M, Shiri I, Zaidi H. Organomics: A Concept Reflecting the Importance of PET/CT Healthy Organ Radiomics in Non-Small Cell Lung Cancer Prognosis Prediction Using Machine Learning. Clin Nucl Med 2024; 49:899-908. [PMID: 39192505 DOI: 10.1097/rlu.0000000000005400] [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: 08/29/2024]
Abstract
PURPOSE Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms. PATIENTS AND METHODS This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric. RESULTS For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them. CONCLUSIONS The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.
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Affiliation(s)
- Yazdan Salimi
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Amirhosein Sanaat
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
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Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz MC, Wiestler B. Resolving spatial response heterogeneity in glioblastoma. Eur J Nucl Med Mol Imaging 2024; 51:3685-3695. [PMID: 38837060 PMCID: PMC11445274 DOI: 10.1007/s00259-024-06782-y] [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: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.
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Affiliation(s)
- Julian Ziegenfeuter
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany.
| | - Claire Delbridge
- Department of Pathology, Technical University of Munich, 81675, München, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Friederike Schmidt-Graf
- Department of Neurology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Dennis Hedderich
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Michael Griessmair
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie Thomas
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Hanno S Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- TranslaTUM, Technical University of Munich, 81675, München, Germany
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Zhou W, Zhou Y, Zhang X, Huang T, Zhang R, Li D, Xie X, Wang Y, Xu M. Development and Validation of an Explainable Machine Learning Model for Identification of Hyper-Functioning Parathyroid Glands from High-Frequency Ultrasonographic Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1506-1514. [PMID: 39054242 DOI: 10.1016/j.ultrasmedbio.2024.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/25/2024] [Accepted: 05/30/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE To develop and validate a machine learning (ML) model based on high-frequency ultrasound (HFUS) images with the aim to identify the functional status of parathyroid glands (PTGs) in secondary hyper-parathyroidism (SHPT) patients. METHODS This retrospective study enrolled 60 SHPT patients (27 female, 33 male; mean age: 51.2 years) with 184 PTGs detected from February 2016 to June 2022. All enrollments underwent single-photon emission computed tomography/computed tomography and contrast-enhanced ultrasound examinations. The PTGs were randomly divided into training (n = 147) and testing datasets (n = 37). Four effective ML classifiers were used and combined models incorporating multi-modal HFUS visual signs and radiomics features was constructed based on the optimal classifier. Model performance was compared in terms of discrimination, calibration and clinical utility. The Shapley additive explanation method was used to explain and visualize the main predictors of the optimal model. RESULTS This model, using a random forest classifier algorithm, outperformed other classifiers. Based on optimal classifier features, the model constructed from ultrasound visual and ML features achieved a favorable performance in the prediction of hyper-functioning PTGs. Compared with the traditional visual model, the ultrasound-based ML model achieved significant (p = 0.03) improvement (area under the curve: 0.859 vs. 0.629) and higher sensitivity (100.0% vs. 94.1%) and accuracy (86.5% vs. 67.6%). Among the predictors attributed to model development, large size and high echogenic heterogeneity of PTGs in ultrasonographic images were more often associated with high risk of hyper-functioning PTGs. CONCLUSION The ultrasound-based ML model for identifying hyper-functioning PTGs in SHPT patients showed good performance and interpretability using high-frequency ultrasonographic images, which may facilitate clinical management.
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Affiliation(s)
- Wenwen Zhou
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yu Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China
| | - Xiaoer Zhang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Tongyi Huang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Rui Zhang
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Di Li
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xiaoyan Xie
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yi Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China.
| | - Ming Xu
- Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
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Coppes RP, van Dijk LV. Future of Team-based Basic and Translational Science in Radiation Oncology. Semin Radiat Oncol 2024; 34:370-378. [PMID: 39271272 DOI: 10.1016/j.semradonc.2024.07.007] [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: 09/15/2024]
Abstract
To further optimize radiotherapy, a more personalized treatment towards individual patient's risk profiles, dissecting both patient-specific tumor and normal tissue response to multimodality treatments is needed. Novel developments in radiobiology, using in vitro patient-specific complex tissue resembling 3D models and multiomics approaches at a spatial single-cell level, may provide unprecedented insight into the radiation responses of tumors and normal tissue. Here, we describe the necessary team effort, including all disciplines in radiation oncology, to integrate such data into clinical prediction models and link the relatively "big data" from the clinical practice, allowing accurate patient stratification for personalized treatment approaches.
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Affiliation(s)
- R P Coppes
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.; Department of Biomedical Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands..
| | - L V van Dijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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28
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Deng L, Shuai P, Liu Y, Yong T, Liu Y, Li H, Zheng X. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis. Osteoporos Int 2024; 35:1693-1707. [PMID: 38802557 DOI: 10.1007/s00198-024-07136-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
This study aimed to assess the diagnostic accuracy of radiomics for predicting osteoporosis and the quality of radiomic studies. The study protocol was prospectively registered on PROSPERO (CRD42023425058). We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from inception to June 1, 2023, for eligible articles that applied radiomic techniques to diagnosing osteoporosis or abnormal bone mass. Quality and risk of bias of the included studies were evaluated with radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tools. The data analysis utilized the R program with mada, metafor, and meta packages. Ten retrospective studies with 5926 participants were included in the systematic review and meta-analysis. The overall risk of bias and applicability concerns for each domain of the studies were rated as low, except for one study which was considered to have a high risk of flow and time bias. The mean METRICS score was 70.1% (range 49.6-83.2%). There was moderate heterogeneity across studies and meta-regression identified sources of heterogeneity in the data, including imaging modality, feature selection method, and classifier. The pooled diagnostic odds ratio (DOR) under the bivariate random effects model across the studies was 57.22 (95% CI 27.62-118.52). The pooled sensitivity and specificity were 87% (95% CI 81-92%) and 87% (95% CI 77-93%), respectively. The area under the summary receiver operating characteristic curve (AUC) of the radiomic models was 0.94 (range 0.8 to 0.98). The results supported that the radiomic techniques had good accuracy in diagnosing osteoporosis or abnormal bone mass. The application of radiomics in osteoporosis diagnosis needs to be further confirmed by more prospective studies with rigorous adherence to existing guidelines and multicenter validation.
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Affiliation(s)
- Ling Deng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Shuai
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Youren Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Yong
- Department of Medical Information Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuping Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaoxia Zheng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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29
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Xie C, Yu X, Tan N, Zhang J, Su W, Ni W, Li C, Zhao Z, Xiang Z, Shao L, Li H, Wu J, Cao Z, Jin J, Jin X. Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy. Radiother Oncol 2024; 199:110438. [PMID: 39013503 DOI: 10.1016/j.radonc.2024.110438] [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: 02/22/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT) and radiation dose (RD) distribution images. MATERIALS AND METHODS A total of 273 EC patients underwent VMAT were retrospectively reviewed and enrolled from two centers and divided into training (n = 152), internal validation (n = 66), and external validation (n = 55) cohorts, respectively. Radiomic and dosiomic features along with DL features using convolutional neural networks were extracted and screened from CT and RD images to predict RE. The performance of these models was evaluated and compared using the area under curve (AUC) of the receiver operating characteristic curves (ROC). RESULTS There were 5 and 10 radiomic and dosiomic features were screened, respectively. XGBoost achieved a best AUC of 0.703, 0.694 and 0.801, 0.729 with radiomic and dosiomic features in the internal and external validation cohorts, respectively. ResNet34 achieved a best prediction AUC of 0.642, 0.657 and 0.762, 0.737 for radiomics based DL model (DLR) and RD based DL model (DLD) in the internal and external validation cohorts, respectively. Combined model of DLD + Dosiomics + clinical factors achieved a best AUC of 0.913, 0.821 and 0.805 in the training, internal, and external validation cohorts, respectively. CONCLUSION Although the dose was not responsible for the prediction accuracy, the combination of various feature extraction methods was a factor in improving the RE prediction accuracy. Combining DLD with dosiomic features was promising in the pretreatment prediction of RE for EC patients underwent VMAT.
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Affiliation(s)
- Congying Xie
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Xianwen Yu
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Ninghang Tan
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Jicheng Zhang
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Wanyu Su
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Weihua Ni
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, PR China
| | - Chenyu Li
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Zeshuo Zhao
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Ziqing Xiang
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Li Shao
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Heng Li
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China
| | - Jianping Wu
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; Department of Radiotherapy, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou 324000, PR China
| | - Zhuo Cao
- Department of Respiratory, Lishui People's Hospital, Lishui 323000, PR China.
| | - Juebin Jin
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China.
| | - Xiance Jin
- Department of Radiotherapy Center, 1(st) Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, PR China.
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Bian S, Hong W, Su X, Yao F, Yuan Y, Zhang Y, Xie J, Li T, Pan K, Xue Y, Zhang Q, Yu Z, Tang K, Yang Y, Zhuang Y, Lin J, Xu H. A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study. Abdom Radiol (NY) 2024; 49:3747-3757. [PMID: 38890216 DOI: 10.1007/s00261-024-04421-6] [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: 12/22/2023] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT. METHODS Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA. RESULTS The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram. CONCLUSION The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.
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Affiliation(s)
- Shuying Bian
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weifeng Hong
- The Department of Radiology, The People's Hospital of Yuhuan, Yuhuan, China
| | - Xinhui Su
- The Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Yao
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yaping Yuan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yayun Zhang
- The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Xuefu Road, Wenzhou, Zhejiang, China
| | - Jiageng Xie
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- The Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kehua Pan
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yingnan Xue
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiongying Zhang
- The Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhixian Yu
- The Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Xuefu Road, Wenzhou, Zhejiang, China
| | - Yunjun Yang
- The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Xuefu Road, Wenzhou, Zhejiang, China
| | - Yuandi Zhuang
- The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jie Lin
- The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Xuefu Road, Wenzhou, Zhejiang, China
| | - Hui Xu
- The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Xuefu Road, Wenzhou, Zhejiang, China.
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O'Shea R, Withey SJ, Owczarczyk K, Rookyard C, Gossage J, Godfrey E, Jobling C, Parsons SL, Skipworth RJE, Goh V. Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma. Eur Radiol 2024; 34:6919-6928. [PMID: 38526750 PMCID: PMC11399295 DOI: 10.1007/s00330-024-10666-y] [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: 08/29/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival. METHODS Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality. RESULTS A total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p = .04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p > .05). Test sensitivity of 90% was achieved by ClinRad and Stage models only. CONCLUSIONS Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival. CLINICAL RELEVANCE STATEMENT Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own. KEY POINTS • Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features-GLCM_Correlation and GLCM_Contrast-contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival.
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Affiliation(s)
- Robert O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Samuel J Withey
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Radiology, Royal Marsden Hospital NHS Trust, Sutton, Surrey, UK
| | - Kasia Owczarczyk
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Clinical Oncology, Guy's & St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Christopher Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - James Gossage
- Department of Surgery, Guy's & St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Edmund Godfrey
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Craig Jobling
- Department of Radiology, Nottingham University Hospitals NHS Foundation Trust, Nottingham, UK
| | - Simon L Parsons
- Department of Surgery, Nottingham University Hospitals NHS Foundation Trust, Nottingham, UK
| | | | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Radiology, Guy's & St Thomas' Hospitals NHS Foundation Trust, Westminster Bridge Road, London, SE1 7EG, UK.
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Al-Mubarak H, Bane O, Gillingham N, Kyriakakos C, Abboud G, Cuevas J, Gonzalez J, Meilika K, Horowitz A, Huang HHV, Daza J, Fauveau V, Badani K, Viswanath SE, Taouli B, Lewis S. Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study. Abdom Radiol (NY) 2024; 49:3464-3475. [PMID: 38467854 DOI: 10.1007/s00261-024-04212-z] [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: 09/13/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 03/13/2024]
Abstract
OBJECTIVES To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. METHODS 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. RESULTS Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. CONCLUSION Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. CLINICAL RELEVANCE Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.
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Affiliation(s)
- Haitham Al-Mubarak
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Nicolas Gillingham
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai West, New York, NY, 10019, USA
| | - Christopher Kyriakakos
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Ghadi Abboud
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Janette Gonzalez
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Kirolos Meilika
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amir Horowitz
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hsin-Hui Vivien Huang
- Department of Population Sciences and Health Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jorge Daza
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valentin Fauveau
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ketan Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Satish E Viswanath
- Department of Biomedical Engineering, School of Medicine, Case School of Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, Case School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1234, New York, NY, 10029, USA.
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Tran K, Ginzburg D, Hong W, Attenberger U, Ko HS. Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks. Eur Radiol 2024; 34:6527-6543. [PMID: 38625613 PMCID: PMC11399214 DOI: 10.1007/s00330-024-10736-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Lung cancer, the second most common cancer, presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers to improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts to address them with quality scoring tools. OBJECTIVE This study had two objectives: 1) identify radiomics biomarkers in post-radiotherapy stage III/IV nonsmall cell lung cancer (NSCLC) patients, 2) evaluate research quality using the CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score) frameworks, and formulate an amalgamated CLEAR-RQS tool to enhance scientific rigor. MATERIALS AND METHODS A systematic literature review (Jun-Aug 2023, MEDLINE/PubMed/SCOPUS) was conducted concerning stage III/IV NSCLC, radiotherapy, and radiomic features (RF). Extracted data included study design particulars, such as sample size, radiotherapy/CT technique, selected RFs, and endpoints. CLEAR and RQS were merged into a CLEAR-RQS checklist. Three readers appraised articles utilizing CLEAR, RQS, and CLEAR-RQS metrics. RESULTS Out of 871 articles, 11 met the inclusion/exclusion criteria. The Median cohort size was 91 (range: 10-337) with 9 studies being single-center. No common RF were identified. The merged CLEAR-RQS checklist comprised 61 items. Most unreported items were within CLEAR's "methods" and "open-source," and within RQS's "phantom-calibration," "registry-enrolled prospective-trial-design," and "cost-effective-analysis" sections. No study scored above 50% on RQS. Median CLEAR scores were 55.74% (32.33/58 points), and for RQS, 17.59% (6.3/36 points). CLEAR-RQS article ranking fell between CLEAR and RQS and aligned with CLEAR. CONCLUSION Radiomics research in post-radiotherapy stage III/IV NSCLC exhibits variability and frequently low-quality reporting. The formulated CLEAR-RQS checklist may facilitate education and holds promise for enhancing radiomics research quality. CLINICAL RELEVANCE STATEMENT Current radiomics research in the field of stage III/IV postradiotherapy NSCLC is heterogenous, lacking reproducibility, with no identified imaging biomarker. Radiomics research quality assessment tools may enhance scientific rigor and thereby facilitate radiomics translation into clinical practice. KEY POINTS There is heterogenous and low radiomics research quality in postradiotherapy stage III/IV nonsmall cell lung cancer. Barriers to reproducibility are small cohort size, nonvalidated studies, missing technical parameters, and lack of data, code, and model sharing. CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score), and the amalgamated CLEAR-RQS tool are useful frameworks for assessing radiomics research quality and may provide a valuable resource for educational purposes in the field of radiomics.
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Affiliation(s)
- Kevin Tran
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
- Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3052, Australia
| | - Daniel Ginzburg
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Wei Hong
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Hyun Soo Ko
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia.
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany.
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, 305 Grattan St, Melbourne, VIC 3000, Australia.
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Friesen E, Hari K, Sheft M, Thiessen JD, Martin M. Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review. MAGMA (NEW YORK, N.Y.) 2024; 37:765-790. [PMID: 38635150 DOI: 10.1007/s10334-024-01160-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 03/17/2024] [Accepted: 03/26/2024] [Indexed: 04/19/2024]
Abstract
Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.
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Affiliation(s)
- Emma Friesen
- Chemistry, University of Winnipeg, Winnipeg, Canada.
| | - Kamya Hari
- Physics, University of Winnipeg, Winnipeg, Canada
- Electronics and Communication Engineering, SSN College of Engineering, Chennai, India
| | - Maxina Sheft
- Physics, University of Winnipeg, Winnipeg, Canada
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan D Thiessen
- Imaging Program, Lawson Health Research Institute, London, Canada
- Medical Biophysics, Western University, London, Canada
- Medical Imaging, Western University, London, Canada
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Wang F, Chen W, Chen F, Lu J, Xu Y, Fang M, Jiang H. Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics. Sci Rep 2024; 14:22659. [PMID: 39349536 PMCID: PMC11442625 DOI: 10.1038/s41598-024-73331-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 09/16/2024] [Indexed: 10/02/2024] Open
Abstract
The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Wujie Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Fangmin Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Jinlan Lu
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Yanjun Xu
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Min Fang
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Haitao Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China.
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Yao N, Tian Y, Neves DGD, Zhao C, Mesquita CT, Martins WDA, Dos Santos AASMD, Li Y, Han C, Zhu F, Dai N, Zhou W. Incremental Value of Radiomics Features of Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection. KARDIOLOGIIA 2024; 64:96-104. [PMID: 39392272 DOI: 10.18087/cardio.2024.9.n2685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/30/2024] [Indexed: 10/12/2024]
Abstract
INTRODUCTION Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models. MATERIAL AND METHODS The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts. RESULTS For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p<0.001), 19.3 % (p<0.05), and 18.0 % (p<0.05) in the internal validation cohort, and by 0.06 (p<0.001), 18.0 % (p<0.05) and 18.0 % (p<0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features. CONCLUSION This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.
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Affiliation(s)
- Ni Yao
- Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou
| | - Yanhui Tian
- Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou
| | - Daniel Gama das Neves
- Universidade Federal Fluminense, Department of Radiology; DASA Complexo Hospitalar de Niterói
| | - Chen Zhao
- Michigan Technological University, Department of Applied Computing, Houghton
| | | | | | | | - Yanting Li
- Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou
| | - Chuang Han
- Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou
| | - Fubao Zhu
- Zhengzhou University of Light Industry, School of Computer Science and Technology, Zhengzhou
| | - Neng Dai
- Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Department of Cardiology; National Clinical Research Center for Interventional Medicine
| | - Weihua Zhou
- Michigan Technological University, Department of Applied Computing, Houghton; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton
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Liao CY, Chen YM, Wu YT, Chao HS, Chiu HY, Wang TW, Chen JR, Shiao TH, Lu CF. Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning. Cancer Imaging 2024; 24:129. [PMID: 39350284 PMCID: PMC11440728 DOI: 10.1186/s40644-024-00779-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 09/23/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers. MATERIALS AND METHODS A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index). RESULTS Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses. CONCLUSIONS Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.
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Affiliation(s)
- Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei, 112, Taiwan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei, 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei, 112, Taiwan.
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Choi YH, Kim JE, Lee RW, Kim B, Shin HC, Choe M, Kim Y, Park WY, Jin K, Han S, Paek JH, Kim K. Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy. BMC Med Imaging 2024; 24:256. [PMID: 39333936 PMCID: PMC11428854 DOI: 10.1186/s12880-024-01434-x] [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: 04/05/2024] [Accepted: 09/18/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Kidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade. METHODS We included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI). RESULTS Overall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively. CONCLUSION Significant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.
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Affiliation(s)
- Yoon Ho Choi
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - Ji-Eun Kim
- Division of Nephrology and Hypertension, Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of Korea
| | - Ro Woon Lee
- Department of Radiology, Inha University College of Medicine, Incheon, Republic of Korea
| | - Byoungje Kim
- Department of Radiology, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Hyeong Chan Shin
- Department of Pathology, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Misun Choe
- Department of Pathology, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Yaerim Kim
- Division of Nephrology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Woo Yeong Park
- Division of Nephrology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Kyubok Jin
- Division of Nephrology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Seungyeup Han
- Division of Nephrology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jin Hyuk Paek
- Division of Nephrology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea.
| | - Kipyo Kim
- Division of Nephrology and Hypertension, Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of Korea.
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Wang H, Ge L, Zhou H, Lu X, Yu Z, Peng P, Wang X, Liu A, Chen T, Guo J, Chen Y. Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024:10.1007/s10554-024-03248-y. [PMID: 39317823 DOI: 10.1007/s10554-024-03248-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 09/16/2024] [Indexed: 09/26/2024]
Abstract
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood flow velocity (< 0.25 m/s). Based on machine learning algorithms, cardiac computed tomography angiography (CCTA) radiomics features were adopted to construct prediction models and explore a suitable approach for diagnosing LAA hypercoagulability and adjusting anticoagulation. This study included 652 patients with non-valvular AF. The univariate analysis were used to select meaningful clinical characteristics to predict LAA hypercoagulability. Then 3D Slicer software was adopted to extract radiomics features from CCTA imaging. The radiomics score was calculated using the least absolute shrinkage and selection operator logistic regression analysis to predict LAA hypercoagulability. We then combined clinical characteristics and radiomics scores to construct a nomogram model. Finally, we got prediction models based on machine learning algorithms and logistic regression separately. The area under the receiver operating characteristic curve of radiomics score was 0.8449 in the training set and 0.7998 in the validation set. The nomogram model had a concordance index of 0.838. The final machine-learning based prediction models had good performances (best f1 score = 0.85). Radiomics features of long maximum diameter and high uniformity of Hounsfield unit in left atrial were significant predictors of the hypercoagulable state in LAA, with better predictive efficacy than clinical characteristics. Our combined models based on machine learning were reliable for hypercoagulable state screening and anticoagulation adjustment.
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Affiliation(s)
- Hongsen Wang
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Lan Ge
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Hang Zhou
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xu Lu
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Zhe Yu
- Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100853, China
| | - Peng Peng
- Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100853, China
| | - Xinyan Wang
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Ao Liu
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Tao Chen
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jun Guo
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
| | - Yundai Chen
- Department of Cardiology, The Sixth Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
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Tixier F, Rodriguez D, Jones J, Martin L, Yassall A, Selvaraj B, Islam M, Ostendorf A, Hester M, Ho ML. Radiomic detection of abnormal brain regions in tuberous sclerosis complex. Med Phys 2024. [PMID: 39312593 DOI: 10.1002/mp.17400] [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: 12/21/2023] [Revised: 06/18/2024] [Accepted: 08/22/2024] [Indexed: 09/25/2024] Open
Abstract
BACKGROUND Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. PURPOSE The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. METHODS This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. RESULTS The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p < 0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. CONCLUSION We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.
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Affiliation(s)
- Florent Tixier
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Diana Rodriguez
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Jeremy Jones
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Lisa Martin
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Anthony Yassall
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Bhavani Selvaraj
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Monica Islam
- Department of Neurology, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Adam Ostendorf
- Department of Neurology, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Mark Hester
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Columbus, Ohio, USA
- Department of Neuroscience, College of Medicine, Ohio State University, Columbus, Ohio, USA
| | - Mai-Lan Ho
- Department of Radiology, University of Missouri, Columbia, Missouri, USA
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Yuan N, Hassan MA, Ehrlich K, Weyers BW, Biddle G, Ivanovic V, Raslan OAA, Gui D, Abouyared M, Bewley AF, Birkeland AC, Farwell DG, Marcu L, Qi J. Early Detection of Lymph Node Metastasis Using Primary Head and Neck Cancer Computed Tomography and Fluorescence Lifetime Imaging. Diagnostics (Basel) 2024; 14:2097. [PMID: 39335776 PMCID: PMC11430879 DOI: 10.3390/diagnostics14182097] [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: 08/29/2024] [Revised: 09/18/2024] [Accepted: 09/18/2024] [Indexed: 09/30/2024] Open
Abstract
Objectives: Early detection and accurate diagnosis of lymph node metastasis (LNM) in head and neck cancer (HNC) are crucial for enhancing patient prognosis and survival rates. Current imaging methods have limitations, necessitating new evaluation of new diagnostic techniques. This study investigates the potential of combining pre-operative CT and intra-operative fluorescence lifetime imaging (FLIm) to enhance LNM prediction in HNC using primary tumor signatures. Methods: CT and FLIm data were collected from 46 HNC patients. A total of 42 FLIm features and 924 CT radiomic features were extracted from the primary tumor site and fused. A support vector machine (SVM) model with a radial basis function kernel was trained to predict LNM. Hyperparameter tuning was conducted using 10-fold nested cross-validation. Prediction performance was evaluated using balanced accuracy (bACC) and the area under the ROC curve (AUC). Results: The model, leveraging combined CT and FLIm features, demonstrated improved testing accuracy (bACC: 0.71, AUC: 0.79) over the CT-only (bACC: 0.58, AUC: 0.67) and FLIm-only (bACC: 0.61, AUC: 0.72) models. Feature selection identified that a subset of 10 FLIm and 10 CT features provided optimal predictive capability. Feature contribution analysis identified high-pass and low-pass wavelet-filtered CT images as well as Laguerre coefficients from FLIm as key predictors. Conclusions: Combining CT and FLIm of the primary tumor improves the prediction of HNC LNM compared to either modality alone. Significance: This study underscores the potential of combining pre-operative radiomics with intra-operative FLIm for more accurate LNM prediction in HNC, offering promise to enhance patient outcomes.
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Affiliation(s)
- Nimu Yuan
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
| | - Mohamed A. Hassan
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
| | - Katjana Ehrlich
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
| | - Brent W. Weyers
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
| | - Garrick Biddle
- Department of Radiology—Neuroradiology, University of California, Davis, CA 95817, USA; (G.B.); (O.A.A.R.)
| | - Vladimir Ivanovic
- Department of Neurology, University of California, Davis, CA 95817, USA;
| | - Osama A. A. Raslan
- Department of Radiology—Neuroradiology, University of California, Davis, CA 95817, USA; (G.B.); (O.A.A.R.)
| | - Dorina Gui
- Department of Pathology and Laboratory Medicine, University of California, Davis, CA 95817, USA;
| | - Marianne Abouyared
- Department of Otolaryngology—Head & Neck Surgery, University of California, Davis, CA 95817, USA; (M.A.); (A.F.B.); (A.C.B.)
| | - Arnaud F. Bewley
- Department of Otolaryngology—Head & Neck Surgery, University of California, Davis, CA 95817, USA; (M.A.); (A.F.B.); (A.C.B.)
| | - Andrew C. Birkeland
- Department of Otolaryngology—Head & Neck Surgery, University of California, Davis, CA 95817, USA; (M.A.); (A.F.B.); (A.C.B.)
| | - D. Gregory Farwell
- Department of Otorhinolaryngology–Head and Neck Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
- Department of Neurological Surgery, University of California, Davis, CA 95817, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA; (N.Y.); (M.A.H.); (K.E.); (B.W.W.)
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Shiri I, Balzer S, Baj G, Bernhard B, Hundertmark M, Bakula A, Nakase M, Tomii D, Barbati G, Dobner S, Valenzuela W, Rominger A, Caobelli F, Siontis GCM, Lanz J, Pilgrim T, Windecker S, Stortecky S, Gräni C. Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06922-4. [PMID: 39307861 DOI: 10.1007/s00259-024-06922-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 09/14/2024] [Indexed: 09/28/2024]
Abstract
PURPOSE Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI). METHODS In this prospective, single-center study, consecutive patients with AS were screened with [99mTc]-3,3-diphosphono-1,2-propanodicarboxylic acid ([99mTc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and CT-radiomic features were used for machine learning modeling of ATTR-CM detection and for outcome prediction. Feature selection and classifier algorithms were applied in single- and multi-modality classification scenarios. We split the dataset into training (70%) and testing (30%) samples. Performance was assessed using various metrics across 100 random seeds. RESULTS Out of 263 patients with severe AS (57% males, age 83 ± 4.6years) enrolled, ATTR-CM was confirmed in 27 (10.3%). The lowest performances for detection of concomitant ATTR-CM were observed in invasive measurements and ECG data with area under the curve (AUC) < 0.68. Individual clinical, laboratory, interventional imaging, and CT-radiomics-based features showed moderate performances (AUC 0.70-0.76, sensitivity 0.79-0.82, specificity 0.63-0.72), echocardiography demonstrated good performance (AUC 0.79, sensitivity 0.80, specificity 0.78), and 4D-CT-strain showed the highest performance (AUC 0.85, sensitivity 0.90, specificity 0.74). The multi-modality model (AUC 0.84, sensitivity 0.87, specificity 0.76) did not outperform the model performance based on 4D-CT-strain only data (p-value > 0.05). The multi-modality model adequately discriminated low and high-risk individuals for all-cause mortality at a mean follow-up of 13 months. CONCLUSION Artificial intelligence-based models using collected pre-TAVI evaluation data can effectively detect ATTR-CM in patients with severe AS, offering an alternative diagnostic strategy to scintigraphy and myocardial biopsy.
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Affiliation(s)
- Isaac Shiri
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Sebastian Balzer
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Giovanni Baj
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
- Biostatistics Unit, Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Benedikt Bernhard
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Moritz Hundertmark
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Adam Bakula
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Masaaki Nakase
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Daijiro Tomii
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Giulia Barbati
- Biostatistics Unit, Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Stephan Dobner
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Waldo Valenzuela
- University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, Bern, 3010, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - Federico Caobelli
- Department of Nuclear Medicine, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - George C M Siontis
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Jonas Lanz
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Thomas Pilgrim
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Stephan Windecker
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Stefan Stortecky
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland.
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Zheng B, Zhao Z, Zheng P, Liu Q, Li S, Jiang X, Huang X, Ye Y, Wang H. The current state of MRI-based radiomics in pituitary adenoma: promising but challenging. Front Endocrinol (Lausanne) 2024; 15:1426781. [PMID: 39371931 PMCID: PMC11449739 DOI: 10.3389/fendo.2024.1426781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 08/30/2024] [Indexed: 10/08/2024] Open
Abstract
In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.
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Affiliation(s)
- Baoping Zheng
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pingping Zheng
- Department of Neurosurgery, People’s Hospital of Biyang County, Zhumadian, China
| | - Qiang Liu
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuang Li
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xing Huang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youfan Ye
- Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haijun Wang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Park J, Joo I, Jeon SK, Kim JM, Park SJ, Yoon SH. Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04581-5. [PMID: 39299987 DOI: 10.1007/s00261-024-04581-5] [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: 08/10/2024] [Revised: 09/07/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024]
Abstract
PURPOSE To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs. METHODS Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC). RESULTS The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement. CONCLUSION Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.
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Affiliation(s)
- Junghoan Park
- Seoul National University, Seoul, Republic of Korea
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Ijin Joo
- Seoul National University, Seoul, Republic of Korea.
- Seoul National University Hospital, Seoul, Republic of Korea.
| | - Sun Kyung Jeon
- Seoul National University, Seoul, Republic of Korea
- Seoul National University Hospital, Seoul, Republic of Korea
| | | | - Sang Joon Park
- Seoul National University, Seoul, Republic of Korea
- MEDICAL IP. Co., Ltd, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Seoul National University, Seoul, Republic of Korea
- Seoul National University Hospital, Seoul, Republic of Korea
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Yang T, Zhang L, Sun S, Yao X, Wang L, Ge Y. Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach. Sci Rep 2024; 14:21884. [PMID: 39300101 DOI: 10.1038/s41598-024-72310-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 09/05/2024] [Indexed: 09/22/2024] Open
Abstract
Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-Acquired Pneumonia (SCAP). A total of 174 CAP patients were included in the study, with 64 cases classified as SCAP. Radiomic features were extracted from chest CT scans using radiomics techniques, and screened to remove irrelevant features. Additionally, clinical indicators of patients were similarly screened and constituted the clinical feature set. Subsequently, eight common machine learning models were employed to complete the SCAP identification task. Specifically, interpretability analysis was conducted on the models. In the end, we screened out 15 radiomic features (such as LeastAxisLength, Maximum2DDiameterColumn and ZonePercentage) and two clinical features: Lymphocyte (p = 0.041) and Albumin (p = 0.044). Using radiomic features as inputs in model predictions yielded the highest AUC of 0.85 on the test set. When using the clinical feature set as model inputs, the AUC was 0.82. Combining the two sets of features as model inputs, Ada Boost achieved the best performance with an AUC of 0.89. Our study demonstrates that combining radiomics and clinical data using machine learning methods can more accurately identify SCAP patients.
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Affiliation(s)
- Tianning Yang
- College of Science, North China University of Science and Technology, Tangshan, Hebei, China
| | - Ling Zhang
- Department of Respiratory Medicine, North China University of Science and Technology, Affiliated Hospital, Tangshan, Hebei, China
| | - Siyi Sun
- Department of Respiratory Medicine, North China University of Science and Technology, Affiliated Hospital, Tangshan, Hebei, China
| | - Xuexin Yao
- Department of Respiratory Medicine, North China University of Science and Technology, Affiliated Hospital, Tangshan, Hebei, China
| | - Lichuan Wang
- College of Science, North China University of Science and Technology, Tangshan, Hebei, China.
| | - Yanlei Ge
- Department of Respiratory Medicine, North China University of Science and Technology, Affiliated Hospital, Tangshan, Hebei, China.
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Wu Y, Qi H, Zhang X, Xing Y. Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events. Acad Radiol 2024:S1076-6332(24)00585-3. [PMID: 39304378 DOI: 10.1016/j.acra.2024.08.022] [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: 05/14/2024] [Revised: 07/29/2024] [Accepted: 08/12/2024] [Indexed: 09/22/2024]
Abstract
RATIONALE AND OBJECTIVE To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years. MATERIALS AND METHODS In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model. CONCLUSION PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.
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Affiliation(s)
- Yue Wu
- Radiological Imaging Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China (Y.W.)
| | - Haicheng Qi
- Medical Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China (H.Q., X.Z., Y.X.)
| | - Xinwei Zhang
- Medical Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China (H.Q., X.Z., Y.X.)
| | - Yan Xing
- Medical Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China (H.Q., X.Z., Y.X.); State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, China (Y.X.).
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Maino C, Vernuccio F, Cannella R, Cristoferi L, Franco PN, Carbone M, Cortese F, Faletti R, De Bernardi E, Inchingolo R, Gatti M, Ippolito D. Non-invasive imaging biomarkers in chronic liver disease. Eur J Radiol 2024; 181:111749. [PMID: 39317002 DOI: 10.1016/j.ejrad.2024.111749] [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: 07/28/2024] [Revised: 08/20/2024] [Accepted: 09/17/2024] [Indexed: 09/26/2024]
Abstract
Chronic liver disease (CLD) is a global and worldwide clinical challenge, considering that different underlying liver entities can lead to hepatic dysfunction. In the past, blood tests and clinical evaluation were the main noninvasive tools used to detect, diagnose and follow-up patients with CLD; in case of clinical suspicion of CLD or unclear diagnosis, liver biopsy has been considered as the reference standard to rule out different chronic liver conditions. Nowadays, noninvasive tests have gained a central role in the clinical pathway. Particularly, liver stiffness measurement (LSM) and cross-sectional imaging techniques can provide transversal information to clinicians, helping them to correctly manage, treat and follow patients during time. Cross-sectional imaging techniques, namely computed tomography (CT) and magnetic resonance imaging (MRI), have plenty of potential. Both techniques allow to compute the liver surface nodularity (LSN), associated with CLDs and risk of decompensation. MRI can also help quantify fatty liver infiltration, mainly with the proton density fat fraction (PDFF) sequences, and detect and quantify fibrosis, especially thanks to elastography (MRE). Advanced techniques, such as intravoxel incoherent motion (IVIM), T1- and T2- mapping are promising tools for detecting fibrosis deposition. Furthermore, the injection of hepatobiliary contrast agents has gained an important role not only in liver lesion characterization but also in assessing liver function, especially in CLDs. Finally, the broad development of radiomics signatures, applied to CT and MR, can be considered the next future approach to CLDs. The aim of this review is to provide a comprehensive overview of the current advancements and applications of both invasive and noninvasive imaging techniques in the evaluation and management of CLD.
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Affiliation(s)
- Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy.
| | - Federica Vernuccio
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, Palermo 90127, Italy
| | - Roberto Cannella
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, Palermo 90127, Italy
| | - Laura Cristoferi
- Department of Gastroenterlogy, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Marco Carbone
- Department of Gastroenterlogy, ASST Grande Ospedale Metropolitano Niguarda, Pizza dell'Ospedale Maggiore 3, 20100 Milano, MI, Italy
| | - Francesco Cortese
- Interventional Radiology Unit, "F. Miulli" General Hospital, Acquaviva delle Fonti 70021, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Department of Medicine and Surgery - University of Milano Bicocca, Via Cadore 33, 20090 Monza, MB, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, "F. Miulli" General Hospital, Acquaviva delle Fonti 70021, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy; Department of Medicine and Surgery - University of Milano Bicocca, Via Cadore 33, 20090 Monza, MB, Italy
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Sherminie LPG, Jayatilake ML, Hewavithana PB, Weerakoon BS, Vijithananda SM. Morphometry-based radiomics for predicting prognosis in soft tissue sarcomas of extremities following radiotherapy. Radiography (Lond) 2024; 30:1501-1507. [PMID: 39293374 DOI: 10.1016/j.radi.2024.09.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/14/2024] [Accepted: 09/06/2024] [Indexed: 09/20/2024]
Abstract
INTRODUCTION Cancer is a leading cause of premature death worldwide. Especially cancers like soft tissue sarcomas of extremities (STSE) pose a challenge in oncologic management. Thus, the assessment of prognosis in patients with such cancers is important to select proper management strategies. Radiomics is a promising approach that has shown a wide range of potential applications including predicting prognosis. This study focused on finding out whether the morphometry-based radiomics features could be used to predict the prognosis of patients with STSE following radiotherapy. METHODS The deidentified images, contours and clinical data from The Cancer Imaging Archive (TCIA) were used to evaluate thirty patients with histologically proven STSE following radiotherapy. Twenty-nine three dimensional (3D) morphometric features were extracted for each patient and the two-sample t-test (one-tailed) with the 95% confidence level was used to determine whether there was a significant difference between the patients who developed recurrence or metastasis (RM) and patients who were recurrence or metastasis-free (RMF) following radiotherapy for each morphometric feature. RESULTS According to the findings, only surface-to-volume ratio demonstrated a significant difference (p-value of 0.029) between the RM and RMF after receiving radiotherapy for STSE. CONCLUSION Only surface-to-volume ratio could be utilized as a predictor for assessing the prognosis of patients with STSE following radiotherapy. IMPLICATIONS FOR PRACTICE The ability to predict the response after radiotherapy can facilitate the decision-making process, which will ultimately improve patient outcomes, especially considering the challenges in the management of STSE. This study provides insight that the integration of morphometry-based radiomics features into radiotherapy practice could be useful to evaluate the prognosis of patients who received radiotherapy for STSE.
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Affiliation(s)
- L P G Sherminie
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Sri Lanka.
| | - M L Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Sri Lanka.
| | - P B Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Sri Lanka.
| | - B S Weerakoon
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Sri Lanka.
| | - S M Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Sri Lanka.
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Lee HJ, Lee JH, Lee JE, Na YM, Park MH, Lee JS, Lim HS. Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI. Sci Rep 2024; 14:21691. [PMID: 39289507 PMCID: PMC11408492 DOI: 10.1038/s41598-024-72581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 09/09/2024] [Indexed: 09/19/2024] Open
Abstract
This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644-0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614-0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699-0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.
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Affiliation(s)
- Hyo-Jae Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jeong Hoon Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jong Eun Lee
- Department of Radiology and the Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Yong Min Na
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
- Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Ji Shin Lee
- Department of Pathology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
- Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea.
- Chonnam National University Medical School, Gwangju, Republic of Korea.
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Qi YJ, Su GH, You C, Zhang X, Xiao Y, Jiang YZ, Shao ZM. Radiomics in breast cancer: Current advances and future directions. Cell Rep Med 2024; 5:101719. [PMID: 39293402 DOI: 10.1016/j.xcrm.2024.101719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/10/2024] [Accepted: 08/14/2024] [Indexed: 09/20/2024]
Abstract
Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.
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Affiliation(s)
- Ying-Jia Qi
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xu Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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