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Alhassan AM. Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1591-1608. [PMID: 38448760 PMCID: PMC11300760 DOI: 10.1007/s10278-024-01030-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 03/08/2024]
Abstract
Identifying indolent and aggressive prostate cancers is a critical problem for optimal treatment. The existing approaches of prostate cancer detection are facing challenges as the techniques rely on ground truth labels with limited accuracy, and histological similarity, and do not consider the disease pathology characteristics, and indefinite differences in appearance between the cancerous and healthy tissue lead to many false positive and false negative interpretations. Hence, this research introduces a comprehensive framework designed to achieve accurate identification and localization of prostate cancers, irrespective of their aggressiveness. This is accomplished through the utilization of a sophisticated multilevel bidirectional long short-term memory (Bi-LSTM) model. The pre-processed images are subjected to multilevel feature map-based U-Net segmentation, bolstered by ResNet-101 and a channel-based attention module that improves the performance. Subsequently, segmented images undergo feature extraction, encompassing various feature types, including statistical features, a global hybrid-based feature map, and a ResNet-101 feature map that enhances the detection accuracy. The extracted features are fed to the multilevel Bi-LSTM model, further optimized through channel and spatial attention mechanisms that offer the effective localization and recognition of complex structures of cancer. Further, the framework represents a promising approach for enhancing the diagnosis and localization of prostate cancers, encompassing both indolent and aggressive cases. Rigorous testing on a distinct dataset demonstrates the model's effectiveness, with performance evaluated through key metrics which are reported as 96.72%, 96.17%, and 96.17% for accuracy, sensitivity, and specificity respectively utilizing the dataset 1. For dataset 2, the model achieves the accuracy, sensitivity, and specificity values of 94.41%, 93.10%, and 94.96% respectively. These results surpass the efficiency of alternative methods.
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Affiliation(s)
- Afnan M Alhassan
- College of Computing and Information Technology, Shaqra University, 11961, Shaqra, Saudi Arabia.
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2
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Calogero AE, Crafa A, Cannarella R, Saleh R, Shah R, Agarwal A. Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians. Asian J Androl 2024:00129336-990000000-00203. [PMID: 38978280 DOI: 10.4103/aja202431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/25/2024] [Indexed: 07/10/2024] Open
Abstract
ABSTRACT Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.
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Affiliation(s)
- Aldo E Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Ramadan Saleh
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag 82524, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag 82511, Egypt
| | - Rupin Shah
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Division of Andrology, Department of Urology, Lilavati Hospital and Research Centre, Mumbai 400050, India
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2024; 34:180-196. [PMID: 36376203 PMCID: PMC11156786 DOI: 10.1016/j.zemedi.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.
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Affiliation(s)
- Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany.
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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Huang J, He C, Xu P, Song B, Zhao H, Yin B, He M, Lu X, Wu J, Wang H. Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study. World J Urol 2024; 42:275. [PMID: 38689190 DOI: 10.1007/s00345-024-04995-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE To develop an early diagnosis model of prostate cancer based on clinical-radiomics to improve the accuracy of imaging diagnosis of prostate cancer. METHODS The multicenter study enrolled a total of 449 patients with prostate cancer from December 2017 to January 2022. We retrospectively collected information from 342 patients who underwent prostate biopsy at Minhang Hospital. We extracted T2WI images through 3D-Slice, and used mask tools to mark the prostate area manually. The radiomics features were extracted by Python using the "Pyradiomics" module. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for data dimensionality reduction and feature selection, and the radiomics score was calculated according to the correlation coefficients. Multivariate logistic regression analysis was used to develop predictive models. We incorporated the radiomics score, PI-RADS, and clinical features, and this was presented as a nomogram. The model was validated using a cohort of 107 patients from the Xuhui Hospital. RESULTS In total, 110 effective radiomics features were extracted. Finally, 9 features were significantly associated with the diagnosis of prostate cancer, from which we calculated the radiomics score. The predictors contained in the individualized prediction nomogram included age, fPSA/tPSA, PI-RADS, and radiomics score. The clinical-radiomics model showed good discrimination in the validation cohort (C-index = 0.88). CONCLUSION This study presents a clinical-radiomics model that incorporates age, fPSA/PSA, PI-RADS, and radiomics score, which can be conveniently used to facilitate individualized prediction of prostate cancer before prostate biopsy.
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Affiliation(s)
- Jiaqi Huang
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Chang He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Peirong Xu
- Department of Urology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
- Department of Urology, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China
| | - Hainan Zhao
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China
| | - Bingde Yin
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Minke He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Xuwei Lu
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Jiawen Wu
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Hang Wang
- Department of Urology, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China.
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Kumar R, Ramachandran A, Mittal BR, Singh H. Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on 68 Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features. Nucl Med Mol Imaging 2024; 58:62-68. [PMID: 38510820 PMCID: PMC10948687 DOI: 10.1007/s13139-023-00832-3] [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: 08/02/2023] [Revised: 12/01/2023] [Accepted: 12/12/2023] [Indexed: 03/22/2024] Open
Abstract
Purpose To assess the utility of convoluted neural network (CNN) in differentiating clinically significant and insignificant prostate cancer in patients with 68 Ga PSMA PET/CT-targeted prostate biopsy-proven prostate cancer. Methods In this retrospective study, 142 patients with clinical suspicion of prostate cancer were evaluated who underwent 68 Ga-PSMA PET/CT imaging followed by 68 Ga-PSMA PET/CT-targeted prostate biopsy from the PSMA-avid prostate lesion. Twenty patients with no PSMA-avid lesions were excluded. Local Image Features Extraction (LifeX) software was used to extract radiomic features (RF) from delayed 68 Ga-PSMA PET/CT images of 122 patients. LifeX failed to extract radiomic features in 24 patients, and the remaining 98 were evaluated. RFs were fed to an in-built CNN of the software for computation and results were achieved. Patients with Gleason Score ≥ 7 on histopathology were labeled clinically significant prostate cancer (csPCa). The diagnostic values of radiomic features were evaluated. Results The csPCa was revealed in 69/98 (70.4%) patients, and insignificant PCa was noticed in 29/98 (29.6%) patients. The software extracted 124 RF from the delayed 68 Ga-PSMA PET/CT images. The accuracy of the CNN was 80.7% to differentiate clinically significant and clinically insignificant prostate cancer, with an error percentage (E %) of 19.3%. The sensitivity, specificity, positive predictive, and negative predictive values were 90.3%, 57.7%, 83.6%, and 71.4%, respectively, to detect csPCa. Conclusion CNN is a feasible pre-biopsy screening tool for identifying clinically significant prostate cancer and can be used as an adjunct in the initial diagnosis and early treatment planning. Supplementary Information The online version contains supplementary material available at 10.1007/s13139-023-00832-3.
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Affiliation(s)
- Rajender Kumar
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012 India
| | - Arivan Ramachandran
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012 India
| | - Bhagwant Rai Mittal
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012 India
| | - Harmandeep Singh
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012 India
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Hamm CA, Baumgärtner GL, Padhani AR, Froböse KP, Dräger F, Beetz NL, Savic LJ, Posch H, Lenk J, Schallenberg S, Maxeiner A, Cash H, Günzel K, Hamm B, Asbach P, Penzkofer T. Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur Radiol 2024:10.1007/s00330-024-10700-z. [PMID: 38538841 DOI: 10.1007/s00330-024-10700-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 04/18/2024]
Abstract
OBJECTIVES To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.
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Affiliation(s)
- Charlie A Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- Berlin Institute of Health (BIH), Berlin, Germany.
| | - Georg L Baumgärtner
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, Middlesex, UK
| | - Konrad P Froböse
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Franziska Dräger
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nick L Beetz
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Lynn J Savic
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Helena Posch
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Julian Lenk
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Andreas Maxeiner
- Department of Urology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Hannes Cash
- Department of Urology, Otto-von-Guericke-University Magdeburg, Germany and PROURO, Berlin, Germany
| | - Karsten Günzel
- Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Patrick Asbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
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Li S, Ye X, Tian H, Ding Z, Cui C, Shi S, Yang Y, Li G, Chen J, Lin Z, Ni Z, Xu J, Dong F. An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer. Postgrad Med J 2024; 100:228-236. [PMID: 38142286 DOI: 10.1093/postmj/qgad127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/25/2023] [Accepted: 11/10/2023] [Indexed: 12/25/2023]
Abstract
PURPOSE We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.
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Affiliation(s)
- Shiyu Li
- Department of Ultrasound, The Second Clinical Medical College of Jinan University, China
| | - Xiuqin Ye
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Zhimin Ding
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Chen Cui
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Siyuan Shi
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Yang Yang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Guoqiu Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Jing Chen
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Ziwei Lin
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Zhipeng Ni
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
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Parillo M, Mallio CA, Van der Molen AJ, Rovira À, Dekkers IA, Karst U, Stroomberg G, Clement O, Gianolio E, Nederveen AJ, Radbruch A, Quattrocchi CC. The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS). MAGMA (NEW YORK, N.Y.) 2024; 37:15-25. [PMID: 37702845 PMCID: PMC10876744 DOI: 10.1007/s10334-023-01113-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 09/14/2023]
Abstract
Among the 28 reporting and data systems (RADS) available in the literature, we identified 15 RADS that can be used in Magnetic Resonance Imaging (MRI). Performing examinations without using gadolinium-based contrast agents (GBCA) has benefits, but GBCA administration is often required to achieve an early and accurate diagnosis. The aim of the present review is to summarize the current role of GBCA in MRI RADS. This overview suggests that GBCA are today required in most of the current RADS and are expected to be used in most MRIs performed in patients with cancer. Dynamic contrast enhancement is required for correct scores calculation in PI-RADS and VI-RADS, although scientific evidence may lead in the future to avoid the GBCA administration in these two RADS. In Bone-RADS, contrast enhancement can be required to classify an aggressive lesion. In RADS scoring on whole body-MRI datasets (MET-RADS-P, MY-RADS and ONCO-RADS), in NS-RADS and in Node-RADS, GBCA administration is optional thanks to the intrinsic high contrast resolution of MRI. Future studies are needed to evaluate the impact of the high T1 relaxivity GBCA on the assignment of RADS scores.
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Affiliation(s)
- Marco Parillo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
| | - Aart J Van der Molen
- Department of Radiology, C-2S, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ilona A Dekkers
- Department of Radiology, C-2S, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Uwe Karst
- Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstr. 48, 48149, Münster, Germany
| | - Gerard Stroomberg
- RIWA-Rijn-Association of River Water Works, Groenendael 6, 3439 LV, Nieuwegein, The Netherlands
| | - Olivier Clement
- Service de Radiologie, Université de Paris, AP-HP, Hôpital Européen Georges Pompidou, DMU Imagina, 20 Rue LeBlanc, 75015, Paris, France
| | - Eliana Gianolio
- Department of Molecular Biotechnologies and Health Science, University of Turin, Via Nizza 52, 10125, Turin, Italy
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Alexander Radbruch
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Carlo Cosimo Quattrocchi
- Centre for Medical Sciences-CISMed, University of Trento, Via S. Maria Maddalena 1, 38122, Trento, Italy.
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9
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Abreu-Gomez J, Lim C, Haider MA. Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions. Radiol Clin North Am 2024; 62:37-51. [PMID: 37973244 DOI: 10.1016/j.rcl.2023.06.008] [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: 11/19/2023]
Abstract
The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.
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Affiliation(s)
- Jorge Abreu-Gomez
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Avenue, Suite 3-920, Toronto, ON M5G 2M9, Canada.
| | - Christopher Lim
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Room AB 279, Toronto, ON M4N 3M5, Canada
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and the Joint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, 600 University Avenue, Toronto, ON, Canada M5G 1X5
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Yu W, Yang L, Zhang F, Liu B, Shi Y, Wang J, Shao X, Chen Y, Yang X, Wang Y. Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume. J Nucl Cardiol 2023; 30:2593-2606. [PMID: 37434084 DOI: 10.1007/s12350-023-03333-0] [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: 01/31/2023] [Accepted: 06/15/2023] [Indexed: 07/13/2023]
Abstract
We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.
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Affiliation(s)
- Wenji Yu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Le Yang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Feifei Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Bao Liu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Jianfeng Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China
| | - Yongjun Chen
- Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Xiaoyu Yang
- Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China.
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11
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Jaouen T, Souchon R, Moldovan PC, Bratan F, Duran A, Hoang-Dinh A, Di Franco F, Debeer S, Dubreuil-Chambardel M, Arfi N, Ruffion A, Colombel M, Crouzet S, Gonindard-Melodelima C, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader. Diagn Interv Imaging 2023; 104:465-476. [PMID: 37345961 DOI: 10.1016/j.diii.2023.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. MATERIALS AND METHODS ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. RESULTS The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively. CONCLUSION The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.
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Affiliation(s)
| | | | - Paul C Moldovan
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Flavie Bratan
- Hôpital Saint Joseph Saint Luc, Department of Radiology, Lyon, 69007, France
| | - Audrey Duran
- Univ Lyon, CNRS, Inserm, INSA Lyon, UCBL, CREATIS, UMR5220, U1294, Villeurbanne, 69100, France
| | - Au Hoang-Dinh
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hanoi Medical University, Department of Radiology, Hanoi, 116001, Vietnam
| | - Florian Di Franco
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Sabine Debeer
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Marine Dubreuil-Chambardel
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Nicolas Arfi
- Hôpital Saint Joseph Saint Luc, Department of Urology, Lyon, 69007, France
| | - Alain Ruffion
- Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Department of Urology, Pierre-Bénite, 69310, France; Equipe 2 - Centre d'Innovation en Cancérologie de Lyon (EA 3738 CICLY), Pierre-Bénite, 69310, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Sud, Pierre-Bénite, 69310, France
| | - Marc Colombel
- Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Sébastien Crouzet
- INSERM, LabTAU, U1032, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Christelle Gonindard-Melodelima
- Université Grenoble Alpes, Laboratoire d'Ecologie Alpine, BP 53, Grenoble 38041, France; CNRS, UMR 5553, BP 53, Grenoble, 38041, France
| | - Olivier Rouvière
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France.
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12
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Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
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Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
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13
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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14
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Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol 2023; 165:110887. [PMID: 37245342 DOI: 10.1016/j.ejrad.2023.110887] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/30/2023]
Abstract
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
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Affiliation(s)
- Heejong Kim
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Shin Won Kang
- Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Daniel J A Margolis
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
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15
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Chaddad A, Tan G, Liang X, Hassan L, Rathore S, Desrosiers C, Katib Y, Niazi T. Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects. Cancers (Basel) 2023; 15:3839. [PMID: 37568655 PMCID: PMC10416937 DOI: 10.3390/cancers15153839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada
| | - Guina Tan
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | - Xiaojuan Liang
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | - Lama Hassan
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | | | - Christian Desrosiers
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada
| | - Yousef Katib
- Department of Radiology, Taibah University, Al Madinah 42361, Saudi Arabia
| | - Tamim Niazi
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
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16
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Lv J, Roy S, Xie M, Yang X, Guo B. Contrast Agents of Magnetic Resonance Imaging and Future Perspective. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2003. [PMID: 37446520 DOI: 10.3390/nano13132003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/01/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
In recent times, magnetic resonance imaging (MRI) has emerged as a highly promising modality for diagnosing severe diseases. Its exceptional spatiotemporal resolution and ease of use have established it as an indispensable clinical diagnostic tool. Nevertheless, there are instances where MRI encounters challenges related to low contrast, necessitating the use of contrast agents (CAs). Significant efforts have been made by scientists to enhance the precision of observing diseased body parts by leveraging the synergistic potential of MRI in conjunction with other imaging techniques and thereby modifying the CAs. In this work, our focus is on elucidating the rational designing approach of CAs and optimizing their compatibility for multimodal imaging and other intelligent applications. Additionally, we emphasize the importance of incorporating various artificial intelligence tools, such as machine learning and deep learning, to explore the future prospects of disease diagnosis using MRI. We also address the limitations associated with these techniques and propose reasonable remedies, with the aim of advancing MRI as a cutting-edge diagnostic tool for the future.
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Affiliation(s)
- Jie Lv
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Shubham Roy
- Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Miao Xie
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Xiulan Yang
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Bing Guo
- Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
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17
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Calimano-Ramirez LF, Virarkar MK, Hernandez M, Ozdemir S, Kumar S, Gopireddy DR, Lall C, Balaji KC, Mete M, Gumus KZ. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol (NY) 2023; 48:2379-2400. [PMID: 37142824 DOI: 10.1007/s00261-023-03924-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.
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Affiliation(s)
- Luis F Calimano-Ramirez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mayur K Virarkar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mauricio Hernandez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Savas Ozdemir
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Sindhu Kumar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Dheeraj R Gopireddy
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - K C Balaji
- Department of Urology, University of Florida College of Medicine, Jacksonville, FL, 32209, USA
| | - Mutlu Mete
- Department of Computer Science and Information System, Texas A&M University-Commerce, Commerce, TX, 75428, USA
| | - Kazim Z Gumus
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA.
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18
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He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
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19
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Thimansson E, Bengtsson J, Baubeta E, Engman J, Flondell-Sité D, Bjartell A, Zackrisson S. Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. Eur Radiol 2023; 33:2519-2528. [PMID: 36371606 PMCID: PMC10017633 DOI: 10.1007/s00330-022-09239-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV. METHODS Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI's 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PVDL) and ellipsoid formula by two radiologists (PVEF1 and PVEF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PVMPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry. RESULTS PVDL showed better agreement and precision than PVEF1 and PVEF2 using the reference standard PVMPE (mean difference [95% limits of agreement] PVDL: -0.33 [-10.80; 10.14], PVEF1: -3.83 [-19.55; 11.89], PVEF2: -3.05 [-18.55; 12.45]) or the PV determined based on specimen weight (PVDL: -4.22 [-22.52; 14.07], PVEF1: -7.89 [-30.50; 14.73], PVEF2: -6.97 [-30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry. CONCLUSION Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. KEY POINTS • A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. • The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.
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Affiliation(s)
- Erik Thimansson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Carl-Bertil Laurells gata 9, SE-205 02, Malmö, Sweden.
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden.
| | - J Bengtsson
- Department of Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Lund, Sweden
| | - E Baubeta
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Carl-Bertil Laurells gata 9, SE-205 02, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Lund, Sweden
| | - J Engman
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Carl-Bertil Laurells gata 9, SE-205 02, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Lund, Sweden
| | - D Flondell-Sité
- Department of Translational Medicine, Urological Cancers, Lund University, Malmö, Sweden
- Department of Urology, Skåne University Hospital, Malmö, Sweden
| | - A Bjartell
- Department of Translational Medicine, Urological Cancers, Lund University, Malmö, Sweden
- Department of Urology, Skåne University Hospital, Malmö, Sweden
| | - S Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Carl-Bertil Laurells gata 9, SE-205 02, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Lund, Sweden
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20
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Zhou Z, Guo W, Liu D, Micha JRN, Song Y, Han S. Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning. Sci Rep 2023; 13:4469. [PMID: 36934139 PMCID: PMC10024716 DOI: 10.1038/s41598-023-31189-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/07/2023] [Indexed: 03/20/2023] Open
Abstract
The reliable predictive markers to identify which patients with advanced non-small cell lung cancer tumors (NSCLC) will achieve durable clinical benefit (DCB) for chemo-immunotherapy are needed. In this retrospective study, we collected radiomics and clinical signatures from 94 patients with advanced NSCLC treated with anti-PD-1/PD-L1 combined with chemotherapy from January 1, 2018 to May 31, 2022. Radiomics variables were extracted from pretreatment CT and selected by Spearman correlation coefficients and clinical features by Logistics regression analysis. We performed effective diagnostic algorithms principal components analysis (PCA) and support vector machine (SVM) to develop an early classification model among DCB and non-durable benefit (NDB) groups. A total of 26 radiomics features and 6 clinical features were selected, and then principal component analysis was used to obtain 6 principal components for SVM building. RC-SVM achieved prediction accuracy with AUC of 0.91 (95% CI 0.87-0.94) in the training set, 0.73 (95% CI 0.61-0.85) in the cross-validation set, 0.84 (95% CI 0.80-0.89) in the external validation set. The new method of RC-SVM model based on radiomics-clinical signatures provides a significant additive value on response prediction in patients with NSCLC preceding chemo-immunotherapy.
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Affiliation(s)
- Zihan Zhou
- School of Medicine, Southeast University, Nanjing, 210009, China
- Department of Respiratory and Critical Care Medicine, Southeast University Zhongda Hospital, Nanjing, 210009, China
| | - Wenjie Guo
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Dingqi Liu
- Department of Radiology, BenQ Medical Center, Nanjing, 210000, China
| | - Jose Ramon Nsue Micha
- School of Medicine, Southeast University, Nanjing, 210009, China
- Department of Respiratory and Critical Care Medicine, Southeast University Zhongda Hospital, Nanjing, 210009, China
| | - Yue Song
- School of Medicine, Southeast University, Nanjing, 210009, China
- Department of Respiratory and Critical Care Medicine, Southeast University Zhongda Hospital, Nanjing, 210009, China
| | - Shuhua Han
- School of Medicine, Southeast University, Nanjing, 210009, China.
- Department of Respiratory and Critical Care Medicine, Southeast University Zhongda Hospital, Nanjing, 210009, China.
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21
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Pasini G, Stefano A, Russo G, Comelli A, Marinozzi F, Bini F. Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics? Diagnostics (Basel) 2023; 13:diagnostics13061167. [PMID: 36980475 PMCID: PMC10046953 DOI: 10.3390/diagnostics13061167] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.
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Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
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22
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Huynh LM, Hwang Y, Taylor O, Baine MJ. The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature. Diagnostics (Basel) 2023; 13:diagnostics13061128. [PMID: 36980436 PMCID: PMC10047271 DOI: 10.3390/diagnostics13061128] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.
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Affiliation(s)
- Linda My Huynh
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Yeagyeong Hwang
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Olivia Taylor
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
| | - Michael J. Baine
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Correspondence:
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23
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Nai YH, Cheong DLH, Roy S, Kok T, Stephenson MC, Schaefferkoetter J, Totman JJ, Conti M, Eriksson L, Robins EG, Wang Z, Chua WY, Ang BWL, Singha AK, Thamboo TP, Chiong E, Reilhac A. Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions. Magn Reson Imaging 2023; 100:64-72. [PMID: 36933775 DOI: 10.1016/j.mri.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/01/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023]
Abstract
INTRODUCTION The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Dennis Lai Hong Cheong
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Sharmili Roy
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Trina Kok
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mary C Stephenson
- Centre for Translational MR, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Josh Schaefferkoetter
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - John J Totman
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Maurizio Conti
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Lars Eriksson
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Edward G Robins
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore BioImaging Consortium, Agency for Science, Technology and Research (A*Star), Singapore
| | - Ziting Wang
- Department of Urology, National University Hospital, Singapore
| | - Wynne Yuru Chua
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | | | | | - Edmund Chiong
- Department of Diagnostic Imaging, National University Hospital, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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25
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Zhang Y, Li W, Zhang Z, Xue Y, Liu YL, Nie K, Su MY, Ye Q. Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput 2023; 61:757-771. [PMID: 36598674 PMCID: PMC10548872 DOI: 10.1007/s11517-022-02759-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023]
Abstract
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists' visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.
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Affiliation(s)
- Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Weikang Li
- Department of Radiology, The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yingnan Xue
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA.
| | - Qiong Ye
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, 230031, Anhui, People's Republic of China.
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Shu X, Liu Y, Qiao X, Ai G, Liu L, Liao J, Deng Z, He X. Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification. Br J Radiol 2023; 96:20220238. [PMID: 36475858 PMCID: PMC9975368 DOI: 10.1259/bjr.20220238] [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: 02/28/2022] [Revised: 10/20/2022] [Accepted: 11/20/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features. METHODS Between August 2016 and May 2021, patients with histologically proven PCa who underwent pre-operative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the European Association of Urology-European Association of Nuclear Medicine-European Society for Radiotherapy and Oncology-European Society of Urogenital Radiology-International Society of Geriatric Oncology guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilising five traditional ML approaches: support vector machine, logistic regression, gradient boosting decision tree, k-nearest neighbour and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis. RESULTS A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89). CONCLUSION This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely. ADVANCES IN KNOWLEDGE The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.
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Affiliation(s)
- Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yunfan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaofeng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guangyong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Liu
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Jun Liao
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Zhengqiao Deng
- College of Big Data & Software Engineering, Chongqing University, Chongqing, China
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Patsanis A, Sunoqrot MRS, Bathen TF, Elschot M. CROPro: a tool for automated cropping of prostate magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024004. [PMID: 36895761 PMCID: PMC9990132 DOI: 10.1117/1.jmi.10.2.024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/09/2023] [Indexed: 03/09/2023] Open
Abstract
Purpose To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images. Approach CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes ( 64 × 64 , 128 × 128 , and 256 × 256 pixels2), pixel spacing ( 0.2 × 0.2 , 0.3 × 0.3 , 0.4 × 0.4 , and 0.5 × 0.5 mm 2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images ( N = 1475 ) from the online available PI-CAI challenge were used to train ( N = 1033 ), validate ( N = 221 ), and test ( N = 221 ) all models. Results Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128 , pixel spacing: 0.2 × 0.2 mm 2 ) achieved the best classification performance ( 0.678 ± 0.006 ). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm 2 ) achieved the best performance ( 0.756 ± 0.009 ). Model performance depended on the cropped area, with optimal size generally larger with center cropping ( ∼ 40 cm 2 ) than random/stride cropping ( ∼ 10 cm 2 ). Conclusion We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.
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Affiliation(s)
- Alexandros Patsanis
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
| | - Mohammed R. S. Sunoqrot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Tone F. Bathen
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Mattijs Elschot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
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Surveillance Value of Apparent Diffusion Coefficient Maps: Multiparametric MRI in Active Surveillance of Prostate Cancer. Cancers (Basel) 2023; 15:cancers15041128. [PMID: 36831471 PMCID: PMC9953850 DOI: 10.3390/cancers15041128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND This study aims to establish the value of apparent diffusion coefficient maps and other magnetic resonance sequences for active surveillance of prostate cancer. The study included 530 men with an average age of 66, who were under surveillance for prostate cancer. We have used multiparametric magnetic resonance imaging with subsequent transperineal biopsy (TPB) to verify the imaging findings. RESULTS We have observed a level of agreement of 67.30% between the apparent diffusion coefficient (ADC) maps, other magnetic resonance sequences, and the biopsy results. The sensitivity of the apparent diffusion coefficient is 97.14%, and the specificity is 37.50%. According to our data, apparent diffusion coefficient is the most accurate sequence, followed by diffusion imaging in prostate cancer detection. CONCLUSIONS Based on our findings we advocate that the apparent diffusion coefficient should be included as an essential part of magnetic resonance scanning protocols for prostate cancer in at least bi-parametric settings. The best option will be apparent diffusion coefficient combined with diffusion imaging and T2 sequences. Further large-scale prospective controlled studies are required to define the precise role of multiparametric and bi-parametric magnetic resonance in the active surveillance of prostate cancer.
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Canellas R, Kohli MD, Westphalen AC. The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging. Curr Oncol Rep 2023; 25:243-250. [PMID: 36749494 DOI: 10.1007/s11912-023-01371-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. RECENT FINDINGS Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.
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Affiliation(s)
- Rodrigo Canellas
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
| | - Marc D Kohli
- Clinical Informatics, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA.,Imaging Informatics, UCSF Health, 500 Parnassus Ave, 3rd Floor, San Francisco, CA, 94143, USA
| | - Antonio C Westphalen
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department of Urology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department Radiation Oncology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
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Zaridis DI, Mylona E, Tachos N, Pezoulas VC, Grigoriadis G, Tsiknakis N, Marias K, Tsiknakis M, Fotiadis DI. Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones. Sci Rep 2023; 13:714. [PMID: 36639671 PMCID: PMC9837765 DOI: 10.1038/s41598-023-27671-8] [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: 09/02/2022] [Accepted: 01/05/2023] [Indexed: 01/14/2023] Open
Abstract
Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models' predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate's gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images.
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Affiliation(s)
- Dimitrios I Zaridis
- Biomedical Research Institute, Foundation for Research and Technology Hellas (FORTH), Ioannina, Greece
| | - Eugenia Mylona
- Biomedical Research Institute, Foundation for Research and Technology Hellas (FORTH), Ioannina, Greece
| | - Nikolaos Tachos
- Biomedical Research Institute, Foundation for Research and Technology Hellas (FORTH), Ioannina, Greece
| | - Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Grigorios Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Nikos Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece
| | - Kostas Marias
- Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece.,Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
| | - Manolis Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece.,Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
| | - Dimitrios I Fotiadis
- Biomedical Research Institute, Foundation for Research and Technology Hellas (FORTH), Ioannina, Greece. .,Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
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Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions. Cancers (Basel) 2022; 14:cancers14246156. [PMID: 36551642 PMCID: PMC9776977 DOI: 10.3390/cancers14246156] [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: 11/08/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
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MiR-125b-5p Targets MTFP1 to Inhibit Cell Proliferation, Migration, and Invasion and Facilitate Cell Apoptosis in Endometrial Carcinoma. Mol Biotechnol 2022; 65:961-969. [DOI: 10.1007/s12033-022-00601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 10/29/2022] [Indexed: 11/16/2022]
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Stamoulou E, Spanakis C, Manikis GC, Karanasiou G, Grigoriadis G, Foukakis T, Tsiknakis M, Fotiadis DI, Marias K. Harmonization Strategies in Multicenter MRI-Based Radiomics. J Imaging 2022; 8:303. [PMID: 36354876 PMCID: PMC9695920 DOI: 10.3390/jimaging8110303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 08/13/2023] Open
Abstract
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.
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Affiliation(s)
- Elisavet Stamoulou
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Constantinos Spanakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Georgios C. Manikis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Georgia Karanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Grigoris Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 451 15 Ioannina, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
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Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers. Radiol Med 2022; 127:1245-1253. [PMID: 36114928 PMCID: PMC9587977 DOI: 10.1007/s11547-022-01555-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Objective To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. Materials and methods A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. Results Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). Conclusions AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
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Villalonga JF, Solari D, Cuocolo R, De Lucia V, Ugga L, Gragnaniello C, Pailler JI, Cervio A, Campero A, Cavallo LM, Cappabianca P. Clinical application of the “sellar barrier’s concept” for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis. Front Surg 2022; 9:934721. [PMID: 36157423 PMCID: PMC9492953 DOI: 10.3389/fsurg.2022.934721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Recently, it was defined that the sellar barrier entity could be identified as a predictor of cerebrospinal fluid (CSF) intraoperative leakage. The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach. Methods We conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli “Federico II,” were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers (i.e., patients with intraoperative fistula, n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets. Results According to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (p = 0.0015), MRI barrier: strong (p = 1.405 × 10−6), MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: weak (p = 2.191 × 10−10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10−8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively. Conclusions There is a true correlation between the type of sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.
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Affiliation(s)
- J. F. Villalonga
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
- Correspondence: J. F. Villalonga
| | - D. Solari
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - R. Cuocolo
- Department of Advanced Biomedical Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - V. De Lucia
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - L. Ugga
- Department of Advanced Biomedical Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - C. Gragnaniello
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
- Department of Neurological Surgery, Swedish Neuroscience Institute, Seattle, WA, United States
| | - J. I. Pailler
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
| | - A. Cervio
- Departamento de Neurocirugía, FLENI, Buenos Aires, Argentina
| | - A. Campero
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
| | - L. M. Cavallo
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - P. Cappabianca
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
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Nsugbe E, Ser HL, Ong HF, Ming LC, Goh KW, Goh BH, Lee WL. On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines. Diagnostics (Basel) 2022; 12:diagnostics12092099. [PMID: 36140500 PMCID: PMC9497845 DOI: 10.3390/diagnostics12092099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.
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Affiliation(s)
- Ejay Nsugbe
- Nsugbe Research Labs, Swindon SN1 3LG, UK
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
| | - Hooi-Leng Ser
- Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway 47500, Malaysia
| | - Huey-Fang Ong
- School of Information Technology, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Long Chiau Ming
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong BE-1410, Brunei
| | - Khang-Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
| | - Bey-Hing Goh
- Biofunctional Molecule Exploratory (BMEX) Research Group, School of Pharmacy, Monash University Malaysia, Subang Jaya 47500, Malaysia
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Wai-Leng Lee
- School of Science, Monash University Malaysia, Subang Jaya 47500, Malaysia
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
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matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model. J Imaging 2022; 8:jimaging8080221. [PMID: 36005464 PMCID: PMC9410206 DOI: 10.3390/jimaging8080221] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 02/07/2023] Open
Abstract
Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive–inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.
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Mammadov O, Akkurt BH, Musigmann M, Ari AP, Blömer DA, Kasap DN, Henssen DJ, Nacul NG, Sartoretti E, Sartoretti T, Backhaus P, Thomas C, Stummer W, Heindel W, Mannil M. Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon 2022; 8:e10023. [PMID: 35965975 PMCID: PMC9364026 DOI: 10.1016/j.heliyon.2022.e10023] [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: 03/07/2022] [Revised: 05/04/2022] [Accepted: 07/18/2022] [Indexed: 10/31/2022] Open
Abstract
Objective Material & methods Results Conclusion Radiomics allows for prediction of pseudoprogression in high-grade gliomas. Use of contrast media boosts the performance of the Radiomics prediction model.
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Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.
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Duan B, Guo Z, Pan L, Xu Z, Chen W. Temporal bone CT-based deep learning models for differential diagnosis of primary ciliary dyskinesia related otitis media and simple otitis media with effusion. Am J Transl Res 2022; 14:4728-4735. [PMID: 35958478 PMCID: PMC9360864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/29/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate the diagnostic value of deep learning (DL) in differentiating otitis media (OM) caused by otitis media with effusion (OME) and primary ciliary dyskinesia (PCD), so as to provide reference for early intervention. METHODS From January 2010 to January 2021, 31 patients with PCD who had temporal bone computed tomography (TBCT) in the Children's Hospital of Fudan University were retrospectively analyzed. Another 30 age-matched cases of OME with TBCT were collected as the control group. The CT imaging signatures of children were observed. Besides, a variety of DL neural network training models were established based on PyTorch, and the optimal models were trained and selected for PCD screening. RESULTS The google net-trained model worked best, with an accuracy of 0.99. Vgg16_bn, vgg19_bn, resnet18, and resnet34; having neural networks with fewer layers, better model effects, with an accuracy rate of 0.86, 0.9, 0.86, and 0.86, respectively. Resnet50 and other neural networks with more layers had relatively poor results. CONCLUSION DL-based CT radiomics can accurately distinguish OM caused by OME from that induced by PCD, which can be used for screening the PCD.
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Affiliation(s)
- Bo Duan
- Department of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan UniversityShanghai 201102, China
| | - Zhuoyao Guo
- Department of Respirology, Children’s Hospital of Fudan UniversityShanghai 201102, China
| | - Lili Pan
- Department of Radiology, Children’s Hospital of Fudan UniversityShanghai 201102, China
| | - Zhengmin Xu
- Department of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan UniversityShanghai 201102, China
| | - Wenxia Chen
- Department of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan UniversityShanghai 201102, China
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Yang L, Li Z, Liang X, Xu J, Cai Y, Huang C, Zhang M, Yao J, Song B. Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion. Front Oncol 2022; 12:934291. [PMID: 35837116 PMCID: PMC9274129 DOI: 10.3389/fonc.2022.934291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To assess the association of radiomics features based on multiparametric MRI (mpMRI) with the proportion of intraductal carcinoma of prostate (IDC-P) and validate the predictive models. Materials and Methods We retrospectively included pre-treatment MR images of prostate cancer (PCa) with IDC components of high proportion (≥10%, hpIDC-P), low proportion (<10%, lpIDC-P), and pure acinar adenocarcinoma (PAC) from our institution for training and internal validation and cooperated cohort for external validation. Normalized images of T2WI, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) map, and dynamic contrast enhanced (DCE) sequences were used for radiomics modeling. The clinical model was built based on serum total prostate specific antigen (tPSA) and Gleason score (GS), and the integrated model was the combination of Rad-score and clinicopathological data. The discrimination ability was assessed by area under the receiver operating characteristic curve (ROC-AUC) in the internal and external validation sets and compared by DeLong test. Results Overall, 97 patients with hpIDC-P, 87 lpIDC-P, and 78 PAC were included for training and internal validation, and 11, 16, and 19 patients for external validation. The integrated model for predicting hpIDC-P got the best ROC-AUC of 0.88 (95%CI = 0.83-0.93) in internal and 0.86 (95%CI = 0.72-1.0) in external validation, which both outperformed clinical models (AUC=0.78, 95% CI = 0.72-0.85, AUC=0.69, 95% CI = 0.5-0.85, respectively) based solely on GS, and the radiomics model (AUC=0.85, 95% CI = 0.79-0.91) was slightly inferior to the integrated model and better than the clinical model in internal dataset. The integrated model for predicting lpIDC-P outperformed both radiomics and clinical models in the internal dataset, while slightly inferior to the integrated model for predicting hpIDC-P. Conclusions Radiomics signature improved differentiation of both hpIDC-P and lpIDC-P versus PAC when compared with the clinical model based on Gleason score, and was validated in an external cohort.
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Affiliation(s)
- Ling Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zhengyan Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xu Liang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yusen Cai
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Mengni Zhang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Jin Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Kaneko M, Fukuda N, Nagano H, Yamada K, Yamada K, Konishi E, Sato Y, Ukimura O. Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study. Prostate 2022; 82:793-803. [PMID: 35192229 DOI: 10.1002/pros.24321] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data. METHODS Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed. RESULTS The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error. CONCLUSIONS We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.
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Affiliation(s)
- Masatomo Kaneko
- Department of Urology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Norio Fukuda
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Hitomi Nagano
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kaori Yamada
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Eiichi Konishi
- Department of Surgical Pathology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshinobu Sato
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Osamu Ukimura
- Department of Urology, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Gatti M, Faletti R, Gentile F, Soncin E, Calleris G, Fornari A, Oderda M, Serafini A, Strazzarino GA, Vissio E, Bergamasco L, Cirillo S, Papotti MG, Gontero P, Fonio P. mEPE-score: a comprehensive grading system for predicting pathologic extraprostatic extension of prostate cancer at multiparametric magnetic resonance imaging. Eur Radiol 2022; 32:4942-4953. [PMID: 35290508 PMCID: PMC9213375 DOI: 10.1007/s00330-022-08595-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 11/24/2022]
Abstract
Objective To investigate the diagnostic accuracy of the PI-RADS v2.1 multiparametric magnetic resonance imaging (mpMRI) features in predicting extraprostatic extension (mEPE) of prostate cancer (PCa), as well as to develop and validate a comprehensive mpMRI-derived score (mEPE-score). Methods We retrospectively reviewed all consecutive patients admitted to two institutions for radical prostatectomy for PCa with available records of mpMRI performed between January 2015 and December 2020. Data from one institution was used for investigating diagnostic performance of each mEPE feature using radical prostatectomy specimens as benchmark. The results were implemented in a mEPE-score as follows: no mEPE features: 1; capsular abutment: 2; irregular or spiculated margin: 3; bulging prostatic contour, or asymmetry of the neurovascular bundles, or tumor-capsule interface > 1.0 cm: 4; ≥ 2 of the previous three parameters or measurable extraprostatic disease: 5. The performance of mEPE features was evaluated using the five diagnostic parameters and ROC curve analysis. Results Two-hundred patients were enrolled at site 1 and 76 at site 2. mEPE features had poor sensitivities ranging from 0.08 (0.00–0.15) to 0.71 (0.59–0.83), whereas specificity ranged from 0.68 (0.58–0.79) to 1.00. mEPE-score showed excellent discriminating ability (AUC > 0.8) and sensitivity = 0.82 and specificity = 0.77 with a threshold of 3. mEPE-score had AUC comparable to ESUR-score (p = 0.59 internal validation; p = 0.82 external validation), higher than or comparable to mEPE-grade (p = 0.04 internal validation; p = 0.58 external validation), and higher than early-and-late-EPE (p < 0.0001 internal and external validation). There were no significant differences between readers having different expertise with EPE-score (p = 0.32) or mEPE-grade (p = 0.45), but there were significant differences for ESUR-score (p = 0.02) and early-versus-late-EPE (p = 0.03). Conclusions The individual mEPE features have low sensitivity and high specificity. The use of mEPE-score allows for consistent and reliable assessment for pathologic EPE. Key Points • Individual PI-RADS v2.1 mpMRI features had poor sensitivities ranging from 0.08 (0.00–0.15) to 0.71 (0.59–0.83), whereas Sp ranged from 0.68 (0.58–0.79) to 1.00. • mEPE-score is an all-inclusive score for the assessment of pEPE with excellent discriminating ability (i.e., AUC > 0.8) and Se = 0.82, Sp = 0.77, PPV = 0.74, and NPV = 0.84 with a threshold of 3. • The diagnostic performance of the expert reader and beginner reader with pEPE-score was comparable (p = 0.32). Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08595-9.
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Affiliation(s)
- Marco Gatti
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy.
| | - Riccardo Faletti
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy
| | - Francesco Gentile
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy
| | - Enrico Soncin
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy
| | - Giorgio Calleris
- Urology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Alberto Fornari
- Radiology Unit, Mauriziano Umberto I Hospital, 10128, Turin, Italy
| | - Marco Oderda
- Urology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Alessandro Serafini
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy
| | | | - Elena Vissio
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Laura Bergamasco
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Stefano Cirillo
- Radiology Unit, Mauriziano Umberto I Hospital, 10128, Turin, Italy
| | - Mauro Giulio Papotti
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Paolo Gontero
- Urology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Paolo Fonio
- Department of Surgical Sciences, Radiology Unit, University of Turin, Via Genova 3, 10126, Turin, Italy
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Rouvière O, Souchon R, Lartizien C, Mansuy A, Magaud L, Colom M, Dubreuil-Chambardel M, Debeer S, Jaouen T, Duran A, Rippert P, Riche B, Monini C, Vlaeminck-Guillem V, Haesebaert J, Rabilloud M, Crouzet S. Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study). BMJ Open 2022; 12:e051274. [PMID: 35140147 PMCID: PMC8830410 DOI: 10.1136/bmjopen-2021-051274] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Prostate multiparametric MRI (mpMRI) has shown good sensitivity in detecting cancers with an International Society of Urological Pathology (ISUP) grade of ≥2. However, it lacks specificity, and its inter-reader reproducibility remains moderate. Biomarkers, such as the Prostate Health Index (PHI), may help select patients for prostate biopsy. Computer-aided diagnosis/detection (CAD) systems may also improve mpMRI interpretation. Different prototypes of CAD systems are currently developed under the Recherche Hospitalo-Universitaire en Santé / Personalized Focused Ultrasound Surgery of Localized Prostate Cancer (RHU PERFUSE) research programme, tackling challenging issues such as robustness across imaging protocols and magnetic resonance (MR) vendors, and ability to characterise cancer aggressiveness. The study primary objective is to evaluate the non-inferiority of the area under the receiver operating characteristic curve of the final CAD system as compared with the Prostate Imaging-Reporting and Data System V.2.1 (PI-RADS V.2.1) in predicting the presence of ISUP ≥2 prostate cancer in patients undergoing prostate biopsy. METHODS This prospective, multicentre, non-inferiority trial will include 420 men with suspected prostate cancer, a prostate-specific antigen level of ≤30 ng/mL and a clinical stage ≤T2 c. Included men will undergo prostate mpMRI that will be interpreted using the PI-RADS V.2.1 score. Then, they will undergo systematic and targeted biopsy. PHI will be assessed before biopsy. At the end of patient inclusion, MR images will be assessed by the final version of the CAD system developed under the RHU PERFUSE programme. Key secondary outcomes include the prediction of ISUP grade ≥2 prostate cancer during a 3-year follow-up, and the number of biopsy procedures saved and ISUP grade ≥2 cancers missed by several diagnostic pathways combining PHI and MRI findings. ETHICS AND DISSEMINATION Ethical approval was obtained from the Comité de Protection des Personnes Nord Ouest III (ID-RCB: 2020-A02785-34). After publication of the results, access to MR images will be possible for testing other CAD systems. TRIAL REGISTRATION NUMBER NCT04732156.
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Affiliation(s)
- Olivier Rouvière
- Université Lyon 1, Université de Lyon, Lyon, France
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
| | | | - Carole Lartizien
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Adeline Mansuy
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Laurent Magaud
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Matthieu Colom
- Direction de la Recherche Clinique et de l'Innovation, Hospices Civils de Lyon, Lyon, France
| | - Marine Dubreuil-Chambardel
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Sabine Debeer
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | - Audrey Duran
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Pascal Rippert
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | | | - Virginie Vlaeminck-Guillem
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biochimie et Biologie Moléculaire Sud, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Julie Haesebaert
- Université Lyon 1, Université de Lyon, Lyon, France
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Lyon, France
| | - Muriel Rabilloud
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | - Sébastien Crouzet
- Université Lyon 1, Université de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Chang CY, Buckless C, Yeh KJ, Torriani M. Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network. Skeletal Radiol 2022; 51:391-399. [PMID: 34291325 DOI: 10.1007/s00256-021-03873-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/09/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs. MATERIALS AND METHODS Our study was IRB-approved and HIPAA-compliant. Cases of confirmed sclerotic bone metastases in chest, abdomen, and pelvis CTs were identified. Images were manually segmented for 3 classes: background, normal bone, and sclerotic lesion(s). If multiple lesions were present on a slice, all lesions were segmented. A total of 600 images were obtained, with a 90/10 training/testing split. Images were stored as 128 × 128 pixel grayscale and the training dataset underwent a processing pipeline of histogram equalization and data augmentation. We trained our model from scratch on Keras/TensorFlow using an 80/20 training/validation split and a U-Net architecture (64 batch size, 100 epochs, dropout 0.25, initial learning rate 0.0001, sigmoid activation). We also tested our model's true negative and false positive rate with 1104 non-pathologic images. Global sensitivity measured model detection of any lesion on a single image, local sensitivity and positive predictive value (PPV) measured model detection of each lesion on a given image, and local specificity measured the false positive rate in non-pathologic bone. RESULTS Dice scores were 0.83 for lesion, 0.96 for non-pathologic bone, and 0.99 for background. Global sensitivity was 95% (57/60), local sensitivity was 92% (89/97), local PPV was 97% (89/92), and local specificity was 87% (958/1104). CONCLUSION A deep convolutional neural network has the potential to assist in detecting sclerotic spinal metastases.
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Affiliation(s)
- Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA.
| | - Colleen Buckless
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA
| | - Kaitlyn J Yeh
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA
| | - Martin Torriani
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6, Boston, MA, 02114, USA
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Li D, Han X, Gao J, Zhang Q, Yang H, Liao S, Guo H, Zhang B. Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations. Front Med (Lausanne) 2022; 8:810995. [PMID: 35096899 PMCID: PMC8793798 DOI: 10.3389/fmed.2021.810995] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Multiparametric magnetic resonance imaging (mpMRI) plays an important role in the diagnosis of prostate cancer (PCa) in the current clinical setting. However, the performance of mpMRI usually varies based on the experience of the radiologists at different levels; thus, the demand for MRI interpretation warrants further analysis. In this study, we developed a deep learning (DL) model to improve PCa diagnostic ability using mpMRI and whole-mount histopathology data. Methods: A total of 739 patients, including 466 with PCa and 273 without PCa, were enrolled from January 2017 to December 2019. The mpMRI (T2 weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences) data were randomly divided into training (n = 659) and validation datasets (n = 80). According to the whole-mount histopathology, a DL model, including independent segmentation and classification networks, was developed to extract the gland and PCa area for PCa diagnosis. The area under the curve (AUC) were used to evaluate the performance of the prostate classification networks. The proposed DL model was subsequently used in clinical practice (independent test dataset; n = 200), and the PCa detective/diagnostic performance between the DL model and different level radiologists was evaluated based on the sensitivity, specificity, precision, and accuracy. Results: The AUC of the prostate classification network was 0.871 in the validation dataset, and it reached 0.797 using the DL model in the test dataset. Furthermore, the sensitivity, specificity, precision, and accuracy of the DL model for diagnosing PCa in the test dataset were 0.710, 0.690, 0.696, and 0.700, respectively. For the junior radiologist without and with DL model assistance, these values were 0.590, 0.700, 0.663, and 0.645 versus 0.790, 0.720, 0.738, and 0.755, respectively. For the senior radiologist, the values were 0.690, 0.770, 0.750, and 0.730 vs. 0.810, 0.840, 0.835, and 0.825, respectively. The diagnosis made with DL model assistance for radiologists were significantly higher than those without assistance (P < 0.05). Conclusion: The diagnostic performance of DL model is higher than that of junior radiologists and can improve PCa diagnostic accuracy in both junior and senior radiologists.
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Affiliation(s)
- Danyan Li
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaowei Han
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jie Gao
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qing Zhang
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Haibo Yang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Hongqian Guo
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
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48
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Bertelli E, Mercatelli L, Marzi C, Pachetti E, Baccini M, Barucci A, Colantonio S, Gherardini L, Lattavo L, Pascali MA, Agostini S, Miele V. Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI. Front Oncol 2022; 11:802964. [PMID: 35096605 PMCID: PMC8792745 DOI: 10.3389/fonc.2021.802964] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/07/2021] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.
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Affiliation(s)
- Elena Bertelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Laura Mercatelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Chiara Marzi
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council of Italy (CNR), Sesto Fiorentino, Italy
| | - Eva Pachetti
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy.,Department of Information Engineering (DII), University of Pisa, Pisa, Italy
| | - Michela Baccini
- "Giuseppe Parenti" Department of Statistics, Computer Science, Applications(DiSIA), University of Florence, Florence, Italy.,Florence Center for Data Science, University of Florence, Florence, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council of Italy (CNR), Sesto Fiorentino, Italy
| | - Sara Colantonio
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy
| | - Luca Gherardini
- "Giuseppe Parenti" Department of Statistics, Computer Science, Applications(DiSIA), University of Florence, Florence, Italy
| | - Lorenzo Lattavo
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Maria Antonietta Pascali
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy
| | - Simone Agostini
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Florence, Italy
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49
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Li H, Lee CH, Chia D, Lin Z, Huang W, Tan CH. Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities. Diagnostics (Basel) 2022; 12:diagnostics12020289. [PMID: 35204380 PMCID: PMC8870978 DOI: 10.3390/diagnostics12020289] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/31/2021] [Accepted: 01/14/2022] [Indexed: 02/04/2023] Open
Abstract
Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.
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Affiliation(s)
- Huanye Li
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (H.L.); (Z.L.)
| | - Chau Hung Lee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore;
| | - David Chia
- Department of Radiation Oncology, National University Cancer Institute (NUH), Singapore 119074, Singapore;
| | - Zhiping Lin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (H.L.); (Z.L.)
| | - Weimin Huang
- Institute for Infocomm Research, A*Star, Singapore 138632, Singapore;
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
- Correspondence:
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50
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Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110170] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.
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