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Tsuboyama T, Yanagawa M, Fujioka T, Fujita S, Ueda D, Ito R, Yamada A, Fushimi Y, Tatsugami F, Nakaura T, Nozaki T, Kamagata K, Matsui Y, Hirata K, Fujima N, Kawamura M, Naganawa S. Recent trends in AI applications for pelvic MRI: a comprehensive review. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01861-4. [PMID: 39096356 DOI: 10.1007/s11547-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/25/2024] [Indexed: 08/05/2024]
Abstract
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
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Affiliation(s)
- Takahiro Tsuboyama
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan.
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Akira Yamada
- Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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2
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Wang R, Li Q, Shi G, Li Q, Zhong D. A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles. PLoS One 2024; 19:e0306549. [PMID: 39083516 PMCID: PMC11290691 DOI: 10.1371/journal.pone.0306549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024] Open
Abstract
Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency. But endometrial cytopathology images often come from different medical centers with different staining styles. It decreases the generalization ability of deep learning models in cytopathology images analysis, leading to poor performance. This study presents a robust automated screening framework for endometrial cancer that can be applied to cytopathology images with different staining styles, and provide an objective diagnostic reference for cytopathologists, thus contributing to clinical treatment. We collected and built the XJTU-EC dataset, the first cytopathology dataset that includes segmentation and classification labels. And we propose an efficient two-stage framework for adapting different staining style images, and screening endometrial cancer at the cellular level. Specifically, in the first stage, a novel CM-UNet is utilized to segment cell clumps, with a channel attention (CA) module and a multi-level semantic supervision (MSS) module. It can ignore staining variance and focus on extracting semantic information for segmentation. In the second stage, we propose a robust and effective classification algorithm based on contrastive learning, ECRNet. By momentum-based updating and adding labeled memory banks, it can reduce most of the false negative results. On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. Our method robustly predicts endometrial cancer on cytopathologic images with different staining styles, which will further advance research in endometrial cancer screening and provide early diagnosis for patients. The code will be available on GitHub.
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Affiliation(s)
- Ruijie Wang
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Qing Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Guizhi Shi
- Laboratory Animal Center, Institute of Biophysics, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, Beijing, China
| | - Qiling Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Dexing Zhong
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
- Pazhou Laboratory, Guangzhou, P.R. China
- Research Institute of Xi’an Jiaotong University, Zhejiang, Hangzhou, P.R. China
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3
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Russo L, Bottazzi S, Kocak B, Zormpas-Petridis K, Gui B, Stanzione A, Imbriaco M, Sala E, Cuocolo R, Ponsiglione A. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools. Eur Radiol 2024:10.1007/s00330-024-10947-6. [PMID: 39014086 DOI: 10.1007/s00330-024-10947-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVE To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
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Affiliation(s)
- Luca Russo
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Bottazzi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Konstantinos Zormpas-Petridis
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evis Sala
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
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4
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Russo L, Bottazzi S, Sala E. Artificial intelligence in female pelvic oncology: tailoring applications to clinical needs. Eur Radiol 2024; 34:4038-4040. [PMID: 37989917 DOI: 10.1007/s00330-023-10455-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/23/2023]
Affiliation(s)
- Luca Russo
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
| | | | - Evis Sala
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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5
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Fujii S, Gonda T, Yunaga H. Clinical Utility of Diffusion-Weighted Imaging in Gynecological Imaging: Revisited. Invest Radiol 2024; 59:78-91. [PMID: 37493356 DOI: 10.1097/rli.0000000000001004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Diffusion-weighted imaging (DWI) is an increasingly valuable sequence in daily clinical practice, providing both functional and morphological information. The use of DWI can help quantify diffusion using the apparent diffusion coefficient, which reflects the physiological features of the tissue and tumor microcirculation. This knowledge is crucial for understanding and interpreting gynecological imaging. This article reviews the clinical utility of DWI for gynecological imaging, highlighting its ability to aid in the detection of endometrial and cervical cancers, as well as tumor extension and metastasis. In addition, DWI can easily detect the solid components of ovarian cancer (including dissemination), assist in the diagnosis of adnexal torsion, and potentially show bone marrow status. Apparent diffusion coefficient measurement is useful for differentiating between endometrial lesions, uterine leiomyomas, and sarcomas, and may provide important information for predicting the prognosis of gynecological cancers.
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Affiliation(s)
- Shinya Fujii
- From the Division of Radiology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan
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6
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Leo E, Stanzione A, Miele M, Cuocolo R, Sica G, Scaglione M, Camera L, Maurea S, Mainenti PP. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows. J Clin Med 2023; 13:226. [PMID: 38202233 PMCID: PMC10779496 DOI: 10.3390/jcm13010226] [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: 12/02/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
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Affiliation(s)
- Elisabetta Leo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Mariaelena Miele
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy
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7
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2023. [PMID: 38146775 DOI: 10.1002/jmri.29161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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Ma C, Zhao Y, Song Q, Meng X, Xu Q, Tian S, Chen L, Wang N, Song Q, Lin L, Wang J, Liu A. Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer. Front Oncol 2023; 13:1280022. [PMID: 38188296 PMCID: PMC10768555 DOI: 10.3389/fonc.2023.1280022] [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: 08/19/2023] [Accepted: 11/15/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC). Methods A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA). Results In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05). Conclusion The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.
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Affiliation(s)
- Changjun Ma
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Ying Zhao
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingling Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Xing Meng
- Dalian Women and Children’s Medical Group, Dalian, China
| | - Qihao Xu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Shifeng Tian
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Lihua Chen
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Nan Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Liangjie Lin
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
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9
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Ying J, Huang W, Fu L, Yang H, Cheng J. Weakly supervised segmentation of uterus by scribble labeling on endometrial cancer MR images. Comput Biol Med 2023; 167:107582. [PMID: 37922606 DOI: 10.1016/j.compbiomed.2023.107582] [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: 05/07/2023] [Revised: 09/28/2023] [Accepted: 10/15/2023] [Indexed: 11/07/2023]
Abstract
Uterine segmentation of endometrial cancer MR images can be a valuable diagnostic tool for gynecologists. However, uterine segmentation based on deep learning relies on artificial pixel-level annotation, which is time-consuming, laborious and subjective. To reduce the dependence on pixel-level annotation, a method of weakly supervised uterine segmentation on endometrial cancer MRI slices is proposed, which only requires scribble label and is enhanced by pseudo-label technology, exponential geodesic distance loss and input disturbance strategy. Specifically, the limitations caused by the shortage of supervision are addressed by dynamically mixing the two outputs of the dual branch network to generate pseudo-labels, expanding supervision information and promoting mutual supervision training. On the other hand, considering the large difference of grayscale intensity between the uterus and surrounding tissues, the exponential geodesic distance loss is introduced to enhance the ability of the network to capture the edge of the uterus. Input disturbance strategies are incorporated to adapt to the flexible and variable characteristics of the uterus and further improve the segmentation performance of the network. The proposed method is evaluated on MRI images from 135 cases of endometrial cancer. Compared with other four weakly supervised segmentation methods, the performance of the proposed method is the best, whose mean DI, HD95, Recall, Precision, ADP are 92.8%, 11.632, 92.7%, 93.6%, 6.5% and increasing by 2.1%, 9.144, 0.6%, 2.4%, 2.9% respectively. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.
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Affiliation(s)
- Jie Ying
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Wei Huang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Haima Yang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jiangzihao Cheng
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
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10
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Guo W, Wang T, Lv B, Jiang J, Liu Y, Zhao P. Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review. J Cancer 2023; 14:3523-3531. [PMID: 38021155 PMCID: PMC10647186 DOI: 10.7150/jca.89347] [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: 08/21/2023] [Accepted: 10/08/2023] [Indexed: 12/01/2023] Open
Abstract
Endometrial cancer (EC) is a common gynecologic malignancy, with a rising trend in related mortality rates. The assessment based on imaging examinations contributes to the preoperative staging and surgical management of EC. However, conventional imaging diagnosis has limitations such as low accuracy and subjectivity. Radiomics, utilizing advanced feature analysis from medical images, extracts more information, ultimately establishing associations between imaging features and disease phenotypes. In recent years, radiomic studies on EC have emerged, employing radiomic features combined with clinical characteristics to model and predict histopathological features, protein expression, and clinical prognosis. This article elaborates on the application of radiomics in EC research and discusses its implications.
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Affiliation(s)
- Wenxiu Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
| | - Tong Wang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Binglin Lv
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Jie Jiang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Yao Liu
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Peng Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
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11
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Oh JH, Kim HG, Lee KM. Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance. Korean J Radiol 2023; 24:698-714. [PMID: 37404112 DOI: 10.3348/kjr.2022.0765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 07/06/2023] Open
Abstract
In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.
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Affiliation(s)
- Jang-Hoon Oh
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea.
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Zhao A, Du X, Yuan S, Shen W, Zhu X, Wang W. Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13081409. [PMID: 37189510 DOI: 10.3390/diagnostics13081409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.
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Affiliation(s)
- Aihua Zhao
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
| | - Xin Du
- Department of Gynecology, Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China
| | - Suzhen Yuan
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenfeng Shen
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
| | - Wenwen Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Mustafa Khan M, ul Islam MS, Siddiqui AA, Qadri MT. Dual deterministic model based on deep neural network for the classification of pneumonia. INTELLIGENT DECISION TECHNOLOGIES 2023. [DOI: 10.3233/idt-220192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Pneumonia is a disease caused by the virus (flu, respiratory Syncytial Virus) or bacteria. It can be fatal if not diagnosed and treated at an early stage. Chest X-rays have been widely utilized to diagnose such abnormalities with high exactitude and are primarily responsible for the augment real-world diagnosis process. Poor availability of authentic data and yardstick-based approaches and studies complicates the comparison process and identifying the safest recognition method. In this paper, a Dual Deterministic Model (DD-M) is proposed based on a Deep Neural network that would identify Pneumonia from chest X-ray and distinguish the cause in case of either viral or bacterial infection at an efficiency equivalent of an active radiologist. To accomplish the automated task of the proposed algorithm, an automatic computer-aided system is necessary. The proposed algorithm incorporates deep learning techniques to understand radiographic imaging better. The results were evaluated after implementing the proposed algorithm where; it reveals various aspects of the chest infected with Pneumonia compared to the healthy individual with approximately 97.45% accuracy and distinguishes between the viral and bacterial infection with the efficiency of 88.41%. The proposed algorithm with an improved image dataset will help the doctors diagnose.
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Lin YC, Lin Y, Huang YL, Ho CY, Chiang HJ, Lu HY, Wang CC, Wang JJ, Ng SH, Lai CH, Lin G. Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights Imaging 2023; 14:14. [PMID: 36690870 PMCID: PMC9871146 DOI: 10.1186/s13244-022-01356-8] [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: 06/22/2022] [Accepted: 12/04/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE To investigate the generalizability of transfer learning (TL) of automated tumor segmentation from cervical cancers toward a universal model for cervical and uterine malignancies in diffusion-weighted magnetic resonance imaging (DWI). METHODS In this retrospective multicenter study, we analyzed pelvic DWI data from 169 and 320 patients with cervical and uterine malignancies and divided them into the training (144 and 256) and testing (25 and 64) datasets, respectively. A pretrained model was established using DeepLab V3 + from the cervical cancer dataset, followed by TL experiments adjusting the training data sizes and fine-tuning layers. The model performance was evaluated using the dice similarity coefficient (DSC). RESULTS In predicting tumor segmentation for all cervical and uterine malignancies, TL models improved the DSCs from the pretrained cervical model (DSC 0.43) when adding 5, 13, 26, and 51 uterine cases for training (DSC improved from 0.57, 0.62, 0.68, 0.70, p < 0.001). Following the crossover at adding 128 cases (DSC 0.71), the model trained by combining data from adding all the 256 patients exhibited the highest DSCs for the combined cervical and uterine datasets (DSC 0.81) and cervical only dataset (DSC 0.91). CONCLUSIONS TL may improve the generalizability of automated tumor segmentation of DWI from a specific cancer type toward multiple types of uterine malignancies especially in limited case numbers.
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Affiliation(s)
- Yu-Chun Lin
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.145695.a0000 0004 1798 0922Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 33302 Taiwan ,grid.454210.60000 0004 1756 1461Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Yenpo Lin
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Yen-Ling Huang
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Chih-Yi Ho
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Hsin-Ju Chiang
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.454210.60000 0004 1756 1461Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Hsin-Ying Lu
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.454210.60000 0004 1756 1461Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Chun-Chieh Wang
- grid.145695.a0000 0004 1798 0922Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 33302 Taiwan ,grid.145695.a0000 0004 1798 0922Department of Radiation Oncology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Jiun-Jie Wang
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.145695.a0000 0004 1798 0922Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 33302 Taiwan
| | - Shu-Hang Ng
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Chyong-Huey Lai
- grid.145695.a0000 0004 1798 0922Gynecologic Cancer Research Center, Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
| | - Gigin Lin
- grid.413801.f0000 0001 0711 0593Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Keelung, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.454210.60000 0004 1756 1461Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan ,grid.145695.a0000 0004 1798 0922Gynecologic Cancer Research Center, Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382 Taiwan
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Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study. Sci Rep 2023; 13:628. [PMID: 36635425 PMCID: PMC9837183 DOI: 10.1038/s41598-023-27883-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective study used multi-vendor MR units and included 170 patients with BC, of whom 140 were assigned to training datasets for the modified U-net model with five-fold cross-validation and 30 to test datasets for assessment of segmentation performance and reproducibility of automatically extracted radiomics features. For model input data, diffusion-weighted images with b = 0 and 1000 s/mm2, ADC maps, and multi-sequence images (b0-b1000-ADC maps) were used. Segmentation accuracy was compared between ours and existing models. The reproducibility of radiomics features on ADC maps was evaluated using intraclass correlation coefficient. The model with multi-sequence images achieved the highest Dice similarity coefficient (DSC) with five-fold cross-validation (mean DSC = 0.83 and 0.79 for the training and validation datasets, respectively). The median (interquartile range) DSC of the test dataset model was 0.81 (0.70-0.88). Radiomics features extracted from manually and automatically segmented BC exhibited good reproducibility. Thus, our U-net model performed highly accurate segmentation of BC, and radiomics features extracted from the automatic segmentation results exhibited high reproducibility.
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Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study. J Pers Med 2022; 12:jpm12111854. [PMID: 36579601 PMCID: PMC9696574 DOI: 10.3390/jpm12111854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/11/2022] [Accepted: 10/31/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology-European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. METHODS This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon-Mann-Whitney. RESULTS A total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high). CONCLUSION MRI-based radiomics has great potential in developing advanced prognostication in EC.
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Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer. Cancers (Basel) 2022; 14:cancers14102372. [PMID: 35625977 PMCID: PMC9139985 DOI: 10.3390/cancers14102372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Uterine cervical cancer (CC) is a leading cause of cancer-related deaths in women worldwide. Pelvic magnetic resonance imaging (MRI) allows the assessment of local tumor extent and guides the choice of primary treatment. MRI tumor segmentation enables whole-volume radiomic tumor profiling, which is potentially useful for prognostication and individualization of therapy in CC. Manual tumor segmentation is, however, labor intensive and thus not part of routine clinical workflow. In the current work, we trained a deep learning (DL) algorithm to automatically segment the primary tumor in CC patients. Although the achieved segmentation performance of the trained DL algorithm is slightly lower than that for human experts, it is still relatively good. This study suggests that automated MRI primary tumor segmentations by DL algorithms without any human interaction is possible in patients with CC. Abstract Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.
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Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
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Teng Y, Ai Y, Liang T, Yu B, Jin J, Xie C, Jin X. The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer. Technol Cancer Res Treat 2022; 21:15330338221099396. [PMID: 35522305 PMCID: PMC9082739 DOI: 10.1177/15330338221099396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, -0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.
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Affiliation(s)
- Yinyan Teng
- Department of Ultrasound imaging, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Yao Ai
- Department of Radiotherapy Center, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Tao Liang
- Department of Radiotherapy Center, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Bing Yu
- Department of Radiotherapy Center, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Juebin Jin
- Department of Medical Engineering, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Congying Xie
- Department of Radiotherapy Center, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
- Department of Radiation and Medical Oncology, Wenzhou Medical University Second Affiliated Hospital, Wenzhou, People’s Republic of China
| | - Xiance Jin
- Department of Radiotherapy Center, Wenzhou Medical University First Affiliated Hospital, Wenzhou, People’s Republic of China
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, People’s Republic of China
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