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Bleker J, Roest C, Yakar D, Huisman H, Kwee TC. The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI. J Magn Reson Imaging 2024; 59:1800-1806. [PMID: 37572098 DOI: 10.1002/jmri.28935] [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: 05/26/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models. PURPOSE To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting. STUDY TYPE Retrospective. POPULATION Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148). FIELD STRENGTH/SEQUENCE 1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps. ASSESSMENT A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models. STATISTICAL TESTS Area under the curve (AUC), DeLong test. The significance level used was 0.05. RESULTS The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67). DATA CONCLUSION Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation). EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jeroen Bleker
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christian Roest
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Derya Yakar
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Amsterdam, The Netherlands
| | - Henkjan Huisman
- Department of Medical Imaging, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Ma K, Chen KZ, Qiao SL. Advances of Layered Double Hydroxide-Based Materials for Tumor Imaging and Therapy. CHEM REC 2024; 24:e202400010. [PMID: 38501833 DOI: 10.1002/tcr.202400010] [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: 01/11/2024] [Revised: 02/22/2024] [Indexed: 03/20/2024]
Abstract
Layered double hydroxides (LDH) are a class of functional anionic clays that typically consist of orthorhombic arrays of metal hydroxides with anions sandwiched between the layers. Due to their unique properties, including high chemical stability, good biocompatibility, controlled drug loading, and enhanced drug bioavailability, LDHs have many potential applications in the medical field. Especially in the fields of bioimaging and tumor therapy. This paper reviews the research progress of LDHs and their nanocomposites in the field of tumor imaging and therapy. First, the structure and advantages of LDH are discussed. Then, several commonly used methods for the preparation of LDH are presented, including co-precipitation, hydrothermal and ion exchange methods. Subsequently, recent advances in layered hydroxides and their nanocomposites for cancer imaging and therapy are highlighted. Finally, based on current research, we summaries the prospects and challenges of layered hydroxides and nanocomposites for cancer diagnosis and therapy.
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Affiliation(s)
- Ke Ma
- Lab of Functional and Biomedical Nanomaterials, College of Materials Science and Engineering, Qingdao University of Science and Technology (QUST), Qingdao, 266042, P. R. China
| | - Ke-Zheng Chen
- Lab of Functional and Biomedical Nanomaterials, College of Materials Science and Engineering, Qingdao University of Science and Technology (QUST), Qingdao, 266042, P. R. China
| | - Sheng-Lin Qiao
- Lab of Functional and Biomedical Nanomaterials, College of Materials Science and Engineering, Qingdao University of Science and Technology (QUST), Qingdao, 266042, P. R. China
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Krauss W, Frey J, Heydorn Lagerlöf J, Lidén M, Thunberg P. Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer. Acta Radiol 2024; 65:307-317. [PMID: 38115809 DOI: 10.1177/02841851231216555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa. PURPOSE To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting. MATERIAL AND METHODS Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves. RESULTS In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171). CONCLUSION PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.
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Affiliation(s)
- Wolfgang Krauss
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Janusz Frey
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob Heydorn Lagerlöf
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Physics, Karlstad Central Hospital, Sweden
| | - Mats Lidén
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Per Thunberg
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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Kawahara D, Murakami Y, Awane S, Emoto Y, Iwashita K, Kubota H, Sasaki R, Nagata Y. Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model. Eur Radiol 2024; 34:1200-1209. [PMID: 37589902 DOI: 10.1007/s00330-023-10020-8] [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: 03/03/2023] [Revised: 05/08/2023] [Accepted: 06/12/2023] [Indexed: 08/18/2023]
Abstract
OBJECTIVES To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features. METHODS The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers. RESULTS A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers. CONCLUSION The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients. CLINICAL RELEVANCE STATEMENT The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed. KEY POINTS • Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.
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Affiliation(s)
- Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Shota Awane
- School of Medicine, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Yuki Emoto
- Department of Radiation Oncology, Hyogo Cancer Center, 70, Kitaoji-Cho 13, Akashi-Shi, Hyogo, Japan
| | - Kazuma Iwashita
- Division of Radiation Oncology, Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuouku, Kobe, Hyogo, 650-0017, Japan
| | - Hikaru Kubota
- Division of Radiation Oncology, Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuouku, Kobe, Hyogo, 650-0017, Japan
| | - Ryohei Sasaki
- Division of Radiation Oncology, Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuouku, Kobe, Hyogo, 650-0017, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
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Nicoletti G, Mazzetti S, Maimone G, Cignini V, Cuocolo R, Faletti R, Gatti M, Imbriaco M, Longo N, Ponsiglione A, Russo F, Serafini A, Stanzione A, Regge D, Giannini V. Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers (Basel) 2024; 16:203. [PMID: 38201630 PMCID: PMC10778513 DOI: 10.3390/cancers16010203] [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: 11/15/2023] [Revised: 12/19/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.
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Affiliation(s)
- Giulia Nicoletti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy;
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Giovanni Maimone
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Valentina Cignini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende, 43, 84081 Baronissi, Italy;
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy;
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Filippo Russo
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Alessandro Serafini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
- Department of Translational Research, Via Risorgimento, 36, University of Pisa, 56126 Pisa, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
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Wu Z, Wang H, Zheng Y, Fei H, Dong C, Wang Z, Ren W, Xu W, Bian T. Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma. Eur Radiol 2023; 33:5594-5605. [PMID: 36973432 DOI: 10.1007/s00330-023-09540-0] [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: 06/23/2022] [Revised: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES Minimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment. METHODS A total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis. RESULTS Sixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set). CONCLUSIONS The lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. KEY POINTS • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM.
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Affiliation(s)
- Zengjie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Yingmei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Hairong Fei
- Department of Hematology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Zhongjun Wang
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Weifeng Ren
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.
| | - Tiantian Bian
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach. Mol Imaging Biol 2023:10.1007/s11307-023-01803-y. [PMID: 36695966 DOI: 10.1007/s11307-023-01803-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023]
Abstract
OBJECTIVES To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. METHODS Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses. RESULTS Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively. CONCLUSION Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.
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Cerdá-Alberich L, Solana J, Mallol P, Ribas G, García-Junco M, Alberich-Bayarri A, Marti-Bonmati L. MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging 2023; 14:11. [PMID: 36645542 PMCID: PMC9842808 DOI: 10.1186/s13244-022-01355-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/20/2022] [Indexed: 01/17/2023] Open
Abstract
The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging.
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Affiliation(s)
- Leonor Cerdá-Alberich
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Jimena Solana
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Pedro Mallol
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Gloria Ribas
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Miguel García-Junco
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Angel Alberich-Bayarri
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain ,Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain
| | - Luis Marti-Bonmati
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
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Rouvière O, Jaouen T, Baseilhac P, Benomar ML, Escande R, Crouzet S, Souchon R. Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review. Diagn Interv Imaging 2022; 104:221-234. [PMID: 36517398 DOI: 10.1016/j.diii.2022.11.005] [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: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI). MATERIALS AND METHODS Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting. RESULTS Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings. CONCLUSION Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.
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Affiliation(s)
- Olivier Rouvière
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France; Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France.
| | | | - Pierre Baseilhac
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Mohammed Lamine Benomar
- LabTAU, INSERM, U1032, Lyon 69003, France; University of Ain Temouchent, Faculty of Science and Technology, Algeria
| | - Raphael Escande
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Sébastien Crouzet
- Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon 69003, France
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11
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Corsi A, De Bernardi E, Bonaffini PA, Franco PN, Nicoletta D, Simonini R, Ippolito D, Perugini G, Occhipinti M, Da Pozzo LF, Roscigno M, Sironi S. Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical-Radiological Model. J Clin Med 2022; 11:6304. [PMID: 36362530 PMCID: PMC9656103 DOI: 10.3390/jcm11216304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 10/29/2023] Open
Abstract
PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016-March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector's model) and 1 T2 feature (Jin's model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers.
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Affiliation(s)
- Andrea Corsi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Elisabetta De Bernardi
- Medicine and Surgery Department, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
- Interdepartmental Research Centre Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, University of Milano-Bicocca, Via Follereau 3, 20854 Vedano al Lambro, Italy
| | - Pietro Andrea Bonaffini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Dario Nicoletta
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Roberto Simonini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Davide Ippolito
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Radiology, San Gerardo Hospital, Via G. B. Pergolesi 33, 20900 Monza, Italy
| | - Giovanna Perugini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | | | - Luigi Filippo Da Pozzo
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Marco Roscigno
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
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Roest C, Fransen SJ, Kwee TC, Yakar D. Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review. Life (Basel) 2022; 12:life12101490. [PMID: 36294928 PMCID: PMC9605624 DOI: 10.3390/life12101490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists. Methods: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Studies investigating DL models for diagnosing clinically significant (cs) PCa on MRI were included. The quality and risk of bias of each study were assessed using the checklist for AI in medical imaging (CLAIM) and QUADAS-2, respectively. Patient level and lesion-based diagnostic performance were separately evaluated by comparing the sensitivity achieved by DL and radiologists at an identical specificity and the false positives per patient, respectively. Results: The final selection consisted of eight studies with a combined 7337 patients. The median study quality with CLAIM was 74.1% (IQR: 70.6–77.6). DL achieved an identical patient-level performance to the radiologists for PI-RADS ≥ 3 (both 97.7%, SD = 2.1%). DL had a lower sensitivity for PI-RADS ≥ 4 (84.2% vs. 88.8%, p = 0.43). The sensitivity of DL for lesion localization was also between 2% and 12.5% lower than that of the radiologists. Conclusions: DL models for the diagnosis of csPCa on MRI appear to approach the performance of experts but currently have a lower sensitivity compared to experienced radiologists. There is a need for studies with larger datasets and for validation on external data.
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Quality of Multicenter Studies Using MRI Radiomics for Diagnosing Clinically Significant Prostate Cancer: A Systematic Review. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070946. [PMID: 35888036 PMCID: PMC9324573 DOI: 10.3390/life12070946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 11/17/2022]
Abstract
Background: Reproducibility and generalization are major challenges for clinically significant prostate cancer modeling using MRI radiomics. Multicenter data seem indispensable to deal with these challenges, but the quality of such studies is currently unknown. The aim of this study was to systematically review the quality of multicenter studies on MRI radiomics for diagnosing clinically significant PCa. Methods: This systematic review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Multicenter studies investigating the value of MRI radiomics for the diagnosis of clinically significant prostate cancer were included. Quality was assessed using the checklist for artificial intelligence in medical imaging (CLAIM) and the radiomics quality score (RQS). CLAIM consisted of 42 equally important items referencing different elements of good practice AI in medical imaging. RQS consisted of 36 points awarded over 16 items related to good practice radiomics. Final CLAIM and RQS scores were percentage-based, allowing for a total quality score consisting of the average of CLAIM and RQS. Results: Four studies were included. The average total CLAIM score was 74.6% and the average RQS was 52.8%. The corresponding average total quality score (CLAIM + RQS) was 63.7%. Conclusions: A very small number of multicenter radiomics PCa classification studies have been performed with the existing studies being of bad or average quality. Good multicenter studies might increase by encouraging preferably prospective data sharing and paying extra care to documentation in regards to reproducibility and clinical utility.
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Sluijter TE, Yakar D, Kwee TC. On-call abdominal ultrasonography: the rate of negative examinations and incidentalomas in a European tertiary care center. Abdom Radiol (NY) 2022; 47:2520-2526. [PMID: 35486165 PMCID: PMC9226090 DOI: 10.1007/s00261-022-03525-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/24/2022]
Abstract
Objectives To determine the proportions of abdominal US examinations during on-call hours that are negative and that contain an incidentaloma, and to explore temporal changes and determinants. Methods This study included 1615 US examinations that were done during on-call hours at a tertiary care center between 2005 and 2017. Results The total proportion of negative US examinations was 49.2% (795/1615). The total proportion of US examinations with an incidentaloma was 8.0% (130/1615). There were no significant temporal changes in either one of these proportions. The likelihood of a negative US examination was significantly higher when requested by anesthesiology [odds ratio (OR) 2.609, P = 0.011], or when the indication for US was focused on gallbladder and biliary ducts (OR 1.556, P = 0.007), transplant (OR 2.371, P = 0.005), trauma (OR 3.274, P < 0.001), or urolithiasis/postrenal obstruction (OR 3.366, P < 0.001). In contrast, US examinations were significantly less likely to be negative when requested by urology (OR 0.423, P = 0.014), or when the indication for US was acute oncology (OR 0.207, P = 0.045) or appendicitis (OR 0.260, P < 0.001). The likelihood of an incidentaloma on US was significantly higher in older patients (OR 1.020 per year of age increase, P < 0.001) or when the liver was evaluated with US (OR 3.522, P < 0.001). Discussion Nearly 50% of abdominal US examinations during on-call hours are negative, and 8% reveal an incidentaloma. Requesting specialty and indication for US affect the likelihood of a negative examination, and higher patient age and liver evaluations increase the chance of detecting an incidentaloma in this setting. These data may potentially be used to improve clinical reasoning and restrain overutilization of imaging. Supplementary Information The online version contains supplementary material available at 10.1007/s00261-022-03525-1.
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Affiliation(s)
- Tim E Sluijter
- Medical Imaging Center, Department of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Derya Yakar
- Medical Imaging Center, Department of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Department of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands.
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