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Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, Makmur A, Ong SJ, Ting YH, Tan JH, Kumar N, Hallinan JTPD. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review. Cancers (Basel) 2024; 16:2988. [PMID: 39272846 PMCID: PMC11394591 DOI: 10.3390/cancers16172988] [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: 07/10/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Aric Lee
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Wei Chuan Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Kuan Ting Dominic Fong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Daoyong David Lai
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Yi Liang Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shao Jin Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Yong Han Ting
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Wang YD, Huang CP, Yang YR, Wu HC, Hsu YJ, Yeh YC, Yeh PC, Wu KC, Kao CH. Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer. Diagnostics (Basel) 2023; 13:3380. [PMID: 37958276 PMCID: PMC10648785 DOI: 10.3390/diagnostics13213380] [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: 09/18/2023] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. METHODS A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image's region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier's effectiveness, the ROC curve area and confusion matrix were employed. RESULTS Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. CONCLUSIONS The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.
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Affiliation(s)
- Yu-De Wang
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404327, Taiwan;
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
| | - Chi-Ping Huang
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
- School of Medicine, China Medical University, Taichung 406040, Taiwan;
| | - You-Rong Yang
- Department of Urology, China Medical University Hospital, Taichung 404327, Taiwan; (C.-P.H.); (Y.-R.Y.)
| | - Hsi-Chin Wu
- School of Medicine, China Medical University, Taichung 406040, Taiwan;
- Department of Urology, China Medical University Beigang Hospital, Yunlin 651012, Taiwan
| | - Yu-Ju Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Yi-Chun Yeh
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Pei-Chun Yeh
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
| | - Kuo-Chen Wu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106319, Taiwan
| | - Chia-Hung Kao
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404327, Taiwan;
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan; (Y.-J.H.); (Y.-C.Y.); (P.-C.Y.); (K.-C.W.)
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
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Li C, Chen H, Zhang B, Fang Y, Sun W, Wu D, Su Z, Shen L, Wei Q. Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Cancers (Basel) 2023; 15:5134. [PMID: 37958309 PMCID: PMC10648149 DOI: 10.3390/cancers15215134] [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: 09/18/2023] [Revised: 10/15/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.
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Affiliation(s)
- Chao Li
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Haiyan Chen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Bicheng Zhang
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Yimin Fang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China;
| | - Wenzheng Sun
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Dang Wu
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Zhuo Su
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Li Shen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Qichun Wei
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
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