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Wang Y, Lin W, Zhuang X, Wang X, He Y, Li L, Lyu G. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review). Oncol Rep 2024; 51:46. [PMID: 38240090 PMCID: PMC10828921 DOI: 10.3892/or.2024.8705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.
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Affiliation(s)
- Yanli Wang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Weihong Lin
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiaoling Zhuang
- Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiali Wang
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| | - Yifang He
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Luhong Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Guorong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
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Vezakis IA, Lambrou GI, Matsopoulos GK. Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach. Cancers (Basel) 2023; 15:cancers15082290. [PMID: 37190217 DOI: 10.3390/cancers15082290] [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: 02/01/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potential for evaluating and classifying histopathological cross-sections. METHODS This study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas. RESULTS The classification performance did not necessarily improve when using larger networks on our dataset. In fact, the smallest network combined with the smallest image input size achieved the best overall performance. When trained using 5-fold cross-validation, the MobileNetV2 network achieved 91% overall accuracy. CONCLUSIONS The present study highlights the importance of careful selection of network and input image size. Our results indicate that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks. The identification of an optimal network and training configuration could greatly improve the accuracy of osteosarcoma diagnoses and ultimately lead to better disease outcomes for patients.
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Affiliation(s)
- Ioannis A Vezakis
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece
| | - George I Lambrou
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece
- Choremeio Research Laboratory, First Department of Pediatrics, National and Kapodistrian University of Athens, Thivon & Levadeias 8, 11527 Athens, Greece
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, Thivon & Levadeias 8, 11527 Athens, Greece
| | - George K Matsopoulos
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece
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Li F, Chen A, Li Z, Gu L, Pan Q, Wang P, Fan Y, Feng J. Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study. Front Neurol 2023; 14:1139096. [PMID: 37077571 PMCID: PMC10109449 DOI: 10.3389/fneur.2023.1139096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/08/2023] [Indexed: 04/05/2023] Open
Abstract
BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.ResultsA total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.ConclusionThe XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.
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Affiliation(s)
- Fengda Li
- Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Anmin Chen
- Department of Nephrology, The First People's Hospital of Jintan, Changzhou, China
| | - Zeyi Li
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Longyuan Gu
- Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qiyang Pan
- Faculty of Informatics, Università della Svizzera italiana, Lugano, Ticino, Switzerland
| | - Pan Wang
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yuechao Fan
- Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- *Correspondence: Yuechao Fan
| | - Jinhong Feng
- Department of Nephrology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- Jinhong Feng
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Zhang H, Xi Q, Zhang F, Li Q, Jiao Z, Ni X. Application of Deep Learning in Cancer Prognosis Prediction Model. Technol Cancer Res Treat 2023; 22:15330338231199287. [PMID: 37709267 PMCID: PMC10503281 DOI: 10.1177/15330338231199287] [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] [Indexed: 09/16/2023] Open
Abstract
As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.
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Affiliation(s)
- Heng Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
| | - Qianyi Xi
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Fan Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Qixuan Li
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Xinye Ni
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
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Liu WC, Li MP, Hong WY, Zhong YX, Sun BL, Huang SH, Liu ZL, Liu JM. A practical dynamic nomogram model for predicting bone metastasis in patients with thyroid cancer. Front Endocrinol (Lausanne) 2023; 14:1142796. [PMID: 36950687 PMCID: PMC10025497 DOI: 10.3389/fendo.2023.1142796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
PURPOSE The aim of this study was to established a dynamic nomogram for assessing the risk of bone metastasis in patients with thyroid cancer (TC) and assist physicians to make accurate clinical decisions. METHODS The clinical data of patients with TC admitted to the First Affiliated hospital of Nanchang University from January 2006 to November 2016 were included in this study. Demographic and clinicopathological parameters of all patients at primary diagnosis were analyzed. Univariate and multivariate logistic regression analysis was applied to build a predictive model incorporating parameters. The discrimination, calibration, and clinical usefulness of the nomogram were evaluated using the C-index, ROC curve, calibration plot, and decision curve analysis. Internal validation was evaluated using the bootstrapping method. RESULTS A total of 565 patients were enrolled in this study, of whom 25 (4.21%) developed bone metastases. Based on logistic regression analysis, age (OR=1.040, P=0.019), hemoglobin (HB) (OR=0.947, P<0.001) and alkaline phosphatase (ALP) (OR=1.006, P=0.002) levels were used to construct the nomogram. The model exhibited good discrimination, with a C-index of 0.825 and good calibration. A C-index value of 0.815 was achieved on interval validation analysis. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at a bone metastases possibility threshold of 1%. CONCLUSIONS This dynamic nomogram, with relatively good accuracy, incorporating age, HB, and ALP, could be conveniently used to facilitate the prediction of bone metastasis risk in patients with TC.
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Affiliation(s)
- Wen-Cai Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- The First Clinical Medical College of Nanchang University, Nanchang, China
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Meng-Pan Li
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Wen-Yuan Hong
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Yan-Xin Zhong
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Bo-Lin Sun
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Shan-Hu Huang
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Li Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Jia-Ming Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
- *Correspondence: Jia-Ming Liu,
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