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Yang X, Gao C, Sun N, Qin X, Liu X, Zhang C. An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer. Front Oncol 2024; 14:1353780. [PMID: 38846980 PMCID: PMC11153703 DOI: 10.3389/fonc.2024.1353780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
Objective The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery. Materials and methods A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature. Results Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model. Conclusion We established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.
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Affiliation(s)
- Xianyue Yang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Chuanfen Gao
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Nian Sun
- Department of Ultrasound, Anhui Provincial Maternity and Child Health Hospital, Hefei, Anhui, China
| | - Xiachuan Qin
- Department of Ultrasound, Nanchong Central Hospital (Beijing Anzhen Hospital Nanchong Hospital), The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Xiaoling Liu
- Department of Ultrasound, Nanchong Central Hospital (Beijing Anzhen Hospital Nanchong Hospital), The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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Cellina M, Irmici G, Pepa GD, Ce M, Chiarpenello V, Alì M, Papa S, Carrafiello G. Radiomics and Artificial Intelligence in Renal Lesion Assessment. Crit Rev Oncog 2024; 29:65-75. [PMID: 38505882 DOI: 10.1615/critrevoncog.2023051084] [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: 03/21/2024]
Abstract
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications. Curr Opin Urol 2023; 33:428-436. [PMID: 37727910 DOI: 10.1097/mou.0000000000001131] [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: 09/21/2023]
Abstract
PURPOSE OF REVIEW Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. RECENT FINDINGS Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. SUMMARY Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.
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Affiliation(s)
- Antoine Valeri
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
- CeRePP, Paris, France
| | - Truong An Nguyen
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
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4
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Wang Y, Ding Y, Liu X, Li X, Jia X, Li J, Zhang H, Song Z, Xu M, Ren J, Sun D. Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study. Cancer Imaging 2023; 23:83. [PMID: 37679806 PMCID: PMC10485937 DOI: 10.1186/s40644-023-00605-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/27/2023] [Indexed: 09/09/2023] Open
Abstract
OBJECTIVE To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). MATERIALS AND METHODS The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. RESULTS A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. CONCLUSION We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.
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Affiliation(s)
- Yuhang Wang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Yun Ding
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Liu
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Li
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China
| | - Xiaoteng Jia
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Jiuzhen Li
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Han Zhang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Zhenchun Song
- Department of Imaging, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Meilin Xu
- Department of Pathology, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Jie Ren
- Graduate School, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Jinnan Hospital, Tianjin, China
| | - Daqiang Sun
- Graduate School, Tianjin Medical University, Tianjin, China.
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China.
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Li Y, Dong B, Yuan P. The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis. Front Oncol 2023; 13:1207175. [PMID: 37746301 PMCID: PMC10513372 DOI: 10.3389/fonc.2023.1207175] [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: 04/17/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Background Malignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches. Results The inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set. Conclusion Machine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.
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Affiliation(s)
| | - Bo Dong
- Department of Orthopedics, Xi’an Honghui Hospital, Xi’an Jiaotong University, Xi’an Shaanxi, China
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Gutiérrez Hidalgo B, Gómez Rivas J, de la Parra I, Marugán MJ, Serrano Á, Hermida Gutiérrez JF, Barrera J, Moreno-Sierra J. The Use of Radiomic Tools in Renal Mass Characterization. Diagnostics (Basel) 2023; 13:2743. [PMID: 37685281 PMCID: PMC10487148 DOI: 10.3390/diagnostics13172743] [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: 06/01/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 09/10/2023] Open
Abstract
The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.
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Affiliation(s)
- Beatriz Gutiérrez Hidalgo
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Gómez Rivas
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Irene de la Parra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - María Jesús Marugán
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Álvaro Serrano
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Juan Fco Hermida Gutiérrez
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
| | - Jerónimo Barrera
- Radiodiagnosis Department, Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain
| | - Jesús Moreno-Sierra
- Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain; (I.d.l.P.); (M.J.M.); (Á.S.); (J.F.H.G.); (J.M.-S.)
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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8
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Fanni SC, Febi M, Colligiani L, Volpi F, Ambrosini I, Tumminello L, Aghakhanyan G, Aringhieri G, Cioni D, Neri E. A first look into radiomics application in testicular imaging: A systematic review. FRONTIERS IN RADIOLOGY 2023; 3:1141499. [PMID: 37492385 PMCID: PMC10365019 DOI: 10.3389/fradi.2023.1141499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/27/2023] [Indexed: 07/27/2023]
Abstract
The aim of this systematic review was to evaluate the state of the art of radiomics in testicular imaging by assessing the quality of radiomic workflow using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A systematic literature search was performed to find potentially relevant articles on the applications of radiomics in testicular imaging, and 6 final articles were extracted. The mean RQS was 11,33 ± 3,88 resulting in a percentage of 31,48% ± 10,78%. Regarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, index test, reference standard criteria and flow-and-timing domain. In conclusion, despite the publication of promising studies, radiomic research on testicular imaging is in its very beginning and still hindered by methodological limitations, and the potential applications of radiomics for this field are still largely unexplored.
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Frank RA, Dawit H, Bossuyt PMM, Leeflang M, Flood TA, Breau RH, McInnes MDF, Schieda N. Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta-analysis. J Magn Reson Imaging 2023; 57:1172-1184. [PMID: 36054467 DOI: 10.1002/jmri.28397] [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: 06/13/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Biparametric (bp)-MRI and multiparametric (mp)-MRI may improve the diagnostic accuracy of renal mass histology. PURPOSE To evaluate the available evidence on the diagnostic accuracy of bp-MRI and mp-MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. STUDY TYPE Systematic review. POPULATION MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. FIELD STRENGTH/SEQUENCE 1.5 or 3 Tesla. ASSESSMENT Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion-weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS-2. STATISTICAL TESTS Meta-analysis using a bivariate logitnormal random effects model. RESULTS We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%-99%), and specificity was 63% (95% CI: 46%-77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%-90%) and specificity was 77% (95% CI: 73%-81%). DATA CONCLUSION Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Robert A Frank
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Haben Dawit
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.,School of Epidemiology, Public Health and Preventative Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Patrick M M Bossuyt
- Amsterdam University Medical Centers, University of Amsterdam, Epidemiology and Data Science, Amsterdam, the Netherlands
| | - Mariska Leeflang
- Amsterdam University Medical Centers, University of Amsterdam, Epidemiology and Data Science, Amsterdam, the Netherlands
| | - Trevor A Flood
- Department of Anatomical Pathology, University of Ottawa, Ottawa, Canada
| | - Rodney H Breau
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.,Department of Surgery, University of Ottawa, Ottawa, Canada
| | - Matthew D F McInnes
- Department of Radiology, University of Ottawa, Ottawa, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nicola Schieda
- Department of Radiology, University of Ottawa, Ottawa, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
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Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:ijms24054615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images. Biomedicines 2023; 11:biomedicines11010193. [PMID: 36672702 PMCID: PMC9855341 DOI: 10.3390/biomedicines11010193] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/18/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023] Open
Abstract
The aim of this work was to compare the classification of cardiac MR-images of AL versus ATTR amyloidosis by neural networks and by experienced human readers. Cine-MR images and late gadolinium enhancement (LGE) images of 120 patients were studied (70 AL and 50 TTR). A VGG16 convolutional neural network (CNN) was trained with a 5-fold cross validation process, taking care to strictly distribute images of a given patient in either the training group or the test group. The analysis was performed at the patient level by averaging the predictions obtained for each image. The classification accuracy obtained between AL and ATTR amyloidosis was 0.750 for cine-CNN, 0.611 for Gado-CNN and between 0.617 and 0.675 for human readers. The corresponding AUC of the ROC curve was 0.839 for cine-CNN, 0.679 for gado-CNN (p < 0.004 vs. cine) and 0.714 for the best human reader (p < 0.007 vs. cine). Logistic regression with cine-CNN and gado-CNN, as well as analysis focused on the specific orientation plane, did not change the overall results. We conclude that cine-CNN leads to significantly better discrimination between AL and ATTR amyloidosis as compared to gado-CNN or human readers, but with lower performance than reported in studies where visual diagnosis is easy, and is currently suboptimal for clinical practice.
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Chang CC, Tang EK, Wei YF, Lin CY, Wu FZ, Wu MT, Liu YS, Yen YT, Ma MC, Tseng YL. Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan. Front Oncol 2023; 13:1105100. [PMID: 37143945 PMCID: PMC10151670 DOI: 10.3389/fonc.2023.1105100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs). Methods A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models. Result In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275). Conclusion Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.
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Affiliation(s)
- Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - En-Kuei Tang
- Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yu-Feng Wei
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Chest Medicine, Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan
| | - Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Education, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Mi-Chia Ma
- Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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Qian W, Li Z, Chen W, Yin H, Zhang J, Xu J, Hu C. RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study. BMC Med Imaging 2022; 22:221. [PMID: 36528577 PMCID: PMC9759891 DOI: 10.1186/s12880-022-00948-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer. METHODS A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models' performance was analyzed by the receiver operating characteristic analysis in the test cohort. RESULTS Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774-0.906] and test cohort (AUC 0.767; 95% CI 0.613-0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821-0.938) and test cohort (AUC 0.844; 95% CI 0.701-0.936). CONCLUSION The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.
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Affiliation(s)
- Weiliang Qian
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, 215006 Jiangsu People’s Republic of China ,grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Zhisen Li
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Weidao Chen
- grid.507939.1Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020 People’s Republic of China
| | - Hongkun Yin
- grid.507939.1Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020 People’s Republic of China
| | - Jibin Zhang
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Jianming Xu
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Chunhong Hu
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, 215006 Jiangsu People’s Republic of China
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15
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Zhu D, Li J, Li Y, Wu J, Zhu L, Li J, Wang Z, Xu J, Dong F, Cheng J. Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors. Front Mol Biosci 2022; 9:982703. [PMID: 36148014 PMCID: PMC9488515 DOI: 10.3389/fmolb.2022.982703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: We aim to establish a deep learning model called multimodal ultrasound fusion network (MUF-Net) based on gray-scale and contrast-enhanced ultrasound (CEUS) images for classifying benign and malignant solid renal tumors automatically and to compare the model’s performance with the assessments by radiologists with different levels of experience.Methods: A retrospective study included the CEUS videos of 181 patients with solid renal tumors (81 benign and 100 malignant tumors) from June 2012 to June 2021. A total of 9794 B-mode and CEUS-mode images were cropped from the CEUS videos. The MUF-Net was proposed to combine gray-scale and CEUS images to differentiate benign and malignant solid renal tumors. In this network, two independent branches were designed to extract features from each of the two modalities, and the features were fused using adaptive weights. Finally, the network output a classification score based on the fused features. The model’s performance was evaluated using five-fold cross-validation and compared with the assessments of the two groups of radiologists with different levels of experience.Results: For the discrimination between benign and malignant solid renal tumors, the junior radiologist group, senior radiologist group, and MUF-Net achieved accuracy of 70.6%, 75.7%, and 80.0%, sensitivity of 89.3%, 95.9%, and 80.4%, specificity of 58.7%, 62.9%, and 79.1%, and area under the receiver operating characteristic curve of 0.740 (95% confidence internal (CI): 0.70–0.75), 0.794 (95% CI: 0.72–0.83), and 0.877 (95% CI: 0.83–0.93), respectively.Conclusion: The MUF-Net model can accurately classify benign and malignant solid renal tumors and achieve better performance than senior radiologists.Key points: The CEUS video data contain the entire tumor microcirculation perfusion characteristics. The proposed MUF-Net based on B-mode and CEUS-mode images can accurately distinguish between benign and malignant solid renal tumors with an area under the receiver operating characteristic curve of 0.877, which surpasses senior radiologists’ assessments by a large margin.
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Affiliation(s)
- Dongmei Zhu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Junyu Li
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Yan Li
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Ji Wu
- Department of Urology Surgery, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Lin Zhu
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Jian Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Zimo Wang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
| | - Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
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16
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Massa'a RN, Stoeckl EM, Lubner MG, Smith D, Mao L, Shapiro DD, Abel EJ, Wentland AL. Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:2896-2904. [PMID: 35723716 DOI: 10.1007/s00261-022-03577-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Solid renal masses are often indeterminate for benignity versus malignancy on magnetic resonance imaging. Such masses are typically evaluated with either percutaneous biopsy or surgical resection. Percutaneous biopsy can be non-diagnostic and some surgically resected lesions are inadvertently benign. PURPOSE To assess the performance of ten machine learning (ML) algorithms trained with MRI-based radiomics features in distinguishing benign from malignant solid renal masses. METHODS Patients with solid renal masses identified on pre-intervention MRI were curated from our institutional database. Masses with a definitive diagnosis via imaging (for angiomyolipomas) or via biopsy or surgical resection (for oncocytomas or renal cell carcinomas) were selected. Each mass was segmented for both T2- and post-contrast T1-weighted images. Radiomics features were derived from the segmented masses for each imaging sequence. Ten ML algorithms were trained with the radiomics features gleaned from each MR sequence, as well as the combination of MR sequences. RESULTS In total, 182 renal masses in 160 patients were included in the study. The support vector machine algorithm trained on radiomics features from T2-weighted images performed superiorly, with an accuracy of 0.80 and an area under the curve (AUC) of 0.79. Linear discriminant analysis (accuracy = 0.84 and AUC = 0.77) and logistic regression (accuracy = 0.78 and AUC = 0.78) algorithms trained on T2-based radiomics features performed similarly. ML algorithms trained on radiomics features from post-contrast T1-weighted images or the combination of radiomics features from T2- and post-contrast T1-weighted images yielded lower performance. CONCLUSION Machine learning models trained with radiomics features derived from T2-weighted images can provide high accuracy for distinguishing benign from malignant solid renal masses. CLINICAL IMPACT Machine learning models derived from MRI-based radiomics features may improve the clinical management of solid renal masses and have the potential to reduce the frequency with which benign solid renal masses are biopsied or surgically resected.
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Affiliation(s)
- Ruben Ngnitewe Massa'a
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - Elizabeth M Stoeckl
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - Meghan G Lubner
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - David Smith
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - Lu Mao
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Daniel D Shapiro
- Department of Urology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - E Jason Abel
- Department of Urology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Andrew L Wentland
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA. .,Department of Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. .,Department of Biomedical Engineering, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
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Jian L, Liu Y, Xie Y, Jiang S, Ye M, Lin H. MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study. Front Oncol 2022; 12:876664. [PMID: 35719934 PMCID: PMC9204342 DOI: 10.3389/fonc.2022.876664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022] Open
Abstract
Objectives Standard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms. Methods A total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models. Results Clinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness. Conclusion The clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.
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Affiliation(s)
- Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yan Liu
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yu Xie
- Department of Urological Surgery, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Shusuan Jiang
- Department of Urological Surgery, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Mingji Ye
- Department of Urological Surgery, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Huashan Lin
- Department of Pharmaceuticals Diagnosis, General Electric (GE) Healthcare, Changsha, China
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18
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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Fan C, Sun K, Min X, Cai W, Lv W, Ma X, Li Y, Chen C, Zhao P, Qiao J, Lu J, Guo Y, Xia L. Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values. Eur J Radiol 2022; 148:110158. [DOI: 10.1016/j.ejrad.2022.110158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/04/2022] [Accepted: 01/11/2022] [Indexed: 11/03/2022]
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Germain P, Vardazaryan A, Padoy N, Labani A, Roy C, Schindler TH, El Ghannudi S. Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR. Diagnostics (Basel) 2021; 12:diagnostics12010069. [PMID: 35054236 PMCID: PMC8774777 DOI: 10.3390/diagnostics12010069] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.
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Affiliation(s)
- Philippe Germain
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
- Correspondence:
| | - Armine Vardazaryan
- ICube, University of Strasbourg, CNRS, 67000 Strasbourg, France; (A.V.); (N.P.)
- IHU (Institut Hopitalo-Universitaire), 67000 Strasbourg, France
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, 67000 Strasbourg, France; (A.V.); (N.P.)
- IHU (Institut Hopitalo-Universitaire), 67000 Strasbourg, France
| | - Aissam Labani
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
| | - Catherine Roy
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
| | - Thomas Hellmut Schindler
- Mallinckrodt Institute of Radiology, Division of Nuclear Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA;
| | - Soraya El Ghannudi
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
- Department of Nuclear Medicine, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France
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