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Wang B, Hu T, Shen R, Liu L, Qiao J, Zhang R, Zhang Z. A 18F-FDG PET/CT based radiomics nomogram for predicting disease-free survival in stage II/III colorectal adenocarcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04515-1. [PMID: 39096393 DOI: 10.1007/s00261-024-04515-1] [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: 06/15/2024] [Revised: 07/23/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024]
Abstract
OBJECTIVES This study aimed to establish a clinical nomogram model based on a radiomics signatures derived from 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET/CT) and clinical parameters to predict disease-free survival (DFS) in patients with stage II/III colorectal adenocarcinoma. Understanding and predicting DFS in these patients is key to optimizing treatment strategies. METHODS A retrospective analysis included 332 cases from July 2011 to July 2021 at The Sixth Affiliated Hospital, Sun Yat-sen University, with PET/CT assessing radiomics features and clinicopathological features. Univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox, and multivariable Cox regression identified recurrence-related radiomics features. We used a weighted radiomics score (Rad-score) and independent risk factors to construct a nomogram. Evaluation involved time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS The nomogram, incorporating Rad-score, pN, and pT demonstrated robust predictive ability for DFS in stage II/III colorectal adenocarcinoma. Training cohort areas under the curve (AUCs) were 0.78, 0.80, and 0.86 at 1, 2, and 3 years, respectively, and validation cohort AUCs were 0.79, 0.75, and 0.73. DCA and calibration curves affirmed the nomogram's clinical relevance. CONCLUSION The 18F-FDG PET/CT based radiomics nomogram, including Rad-score, pN, and pT, effectively predicted tumor recurrence in stage II/III colorectal adenocarcinoma, significantly enhancing prognostic stratification. Our findings highlight the potential of this nomogram as a guide for clinical decision making to improve patient outcomes.
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Affiliation(s)
- Bing Wang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianyuan Hu
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Rongfang Shen
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- The First People's Hospital of Xinjiang Kashgar Area, Kashgar, Xinjiang, China
| | - Lian Liu
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Junwei Qiao
- The First People's Hospital of Xinjiang Kashgar Area, Kashgar, Xinjiang, China
| | - Rongqin Zhang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Zhanwen Zhang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Da-Ano R, Andrade-Miranda G, Tankyevych O, Visvikis D, Conze PH, Rest CCL. Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion. Sci Rep 2024; 14:16720. [PMID: 39030240 PMCID: PMC11271510 DOI: 10.1038/s41598-024-66487-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/01/2024] [Indexed: 07/21/2024] Open
Abstract
Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.
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Affiliation(s)
- Ronrick Da-Ano
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
| | | | - Olena Tankyevych
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- Nuclear Medicine, University of Poitiers, Poitiers, France
| | | | - Pierre-Henri Conze
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- IMT Atlantique, Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, UMR 1101, Inserm, University of Brest, Brest, France
- Nuclear Medicine, University of Poitiers, Poitiers, France
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Yang T, Liu D, Zhang Z, Sa R, Guan F. Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning Models. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:952-964. [PMID: 38321311 PMCID: PMC11169166 DOI: 10.1007/s10278-024-01007-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: 09/13/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 02/08/2024]
Abstract
This study aimed to examine the feasibility of utilizing radiomics models derived from 18F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone 18F-FDG PET/CT scans. Lesions were extracted from PET/CT and randomly divided into training and validation sets. Two different types of models were constructed as follows: features that are extracted from standardized uptake values (SUV)-associated parameters, and CT images were used to build SUV/CT-based model. Features that are derived from PET and CT images were used to build PET/CT-based model. Logistic regression (LR), linear support vector machine, support vector machine with the radial basis function kernel, neural networks, and adaptive boosting were performed as classifiers in each model. In the training sets, 77 patients, and 247 lesions were selected for building the models. In the validation sets, PET/CT-based model demonstrated better performance than that of SUV/CT-based model in the prediction of T-cell lymphoma. LR showed highest accuracy with 0.779 [0.697, 0.860], area under the receiver operating characteristic curve (AUC) with 0.863 [0.762, 0.963], and preferable goodness-of-fit in PET/CT-based model at the patient level. LR also showed best performance with accuracy of 0.838 [0.741, 0.936], AUC of 0.907 [0.839, 0.976], and preferable goodness-of-fit in PET/CT-based model at the lesion level. 18F-FDG PET/CT-based radiomics models with different machine learning classifiers were able to screen T-cell lymphoma in children with high accuracy, AUC, and preferable goodness-of-fit, providing incremental value compared with SUV-associated features.
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Affiliation(s)
- Taiyu Yang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Danyan Liu
- Department of Radiology, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Zexu Zhang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Ri Sa
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
| | - Feng Guan
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
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Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [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: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
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Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
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5
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Hu H, Chi JC, Zhai B, Guo JH. CT-based radiomics analysis to predict local progression of recurrent colorectal liver metastases after microwave ablation. Medicine (Baltimore) 2023; 102:e36586. [PMID: 38206750 PMCID: PMC10754583 DOI: 10.1097/md.0000000000036586] [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: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
The objective of this study is to establish and validate a radiomics nomogram for prediction of local tumor progression (LTP) after microwave ablation (MWA) for recurrent colorectal liver metastases (CRLM) after hepatic resection. We included 318 consecutive recurrent CRLM patients (216 of training while 102 of validation cohort) with contrast-enhanced computerized tomography images treated with MWA between January 2014 and October 2018. Support vector machine-generated radiomics signature was incorporated together with clinical information to establish a radiomics nomogram. Our constructed radiomics signature including 15 features (first-order intensity statistics features, shape and size-based features, gray level size zone/dependence matrix features) performed well in assessing LTP for both cohorts. With regard to its predictive performance, its C-index was 0.912, compared to the clinical or radiomics models only (c-statistic 0.89 and 0.75, respectively) in the training cohort. In the validation cohort, the radiomics nomogram had better performance (area under the curve = 0.89) compared to the radiomics and clinical models (0.85 and 0.69). According to decision curve analysis, our as-constructed radiomics nomogram showed high clinical utility. As revealed by survival analysis, LTP showed worse progression-free survival (3-year progression-free survival 42.6% vs 78.4%, P < .01). High-risk patients identified using this radiomics signature exhibited worse LTP compared with low-risk patients (3-year LTP 80.2% vs 48.6%, P < .01). A radiomics-based nomogram of pre-ablation computerized tomography imaging may be the precious biomarker model for predicting LTP and personalized risk stratification for recurrent CRLM after hepatic resection treated by MWA.
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Affiliation(s)
- Hao Hu
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing, China
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Chang Chi
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bo Zhai
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jin He Guo
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing, China
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Xia Y, Zhang B, Zhang Y. Deep survival analysis using pseudo values and its application to predict the recurrence of stage IV colorectal cancer after tumor resection. Comput Methods Biomech Biomed Engin 2023:1-10. [PMID: 37916498 DOI: 10.1080/10255842.2023.2275246] [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: 05/09/2023] [Accepted: 10/18/2023] [Indexed: 11/03/2023]
Abstract
An improved DeepSurv model is proposed for predicting the prognosis of colorectal cancer patients at stage IV. Our model, called as PseudoDeepSurv, is optimized by a novel loss function, which is the combination of the average negative log partial likelihood and the mean-squared error derived from the pseudo-observations approach. The public BioStudies dataset including 999 patients was utilized for performance evaluation. Our PseudoDeepSurv model produced a C-index of 0.684 and 0.633 on the training and testing dataset, respectively. While for the original DeepSurv model, the corresponding values are 0.671 and 0.618, respectively.
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Affiliation(s)
- Yi Xia
- School of Electrical Engineering and Automation, Anhui University, Hefei, China
| | - Baifu Zhang
- School of Electrical Engineering and Automation, Anhui University, Hefei, China
| | - Yongliang Zhang
- Health Management Center, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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7
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Li M, Xu G, Chen Q, Xue T, Peng H, Wang Y, Shi H, Duan S, Feng F. Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study. Acad Radiol 2023; 30:1572-1583. [PMID: 36566155 DOI: 10.1016/j.acra.2022.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a computed tomography (CT)-based radiomics nomogram for the preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients with colon cancer. MATERIALS AND METHODS This retrospective study included 383 consecutive patients with colon cancer from two centers. Radiomics features were extracted from portal venous phase CT images. Least absolute shrinkage and selection operator regression was applied for feature selection and radiomics signature construction. The multivariate logistic regression model was used to establish a radiomics nomogram. The performance of the nomogram was assessed by using receiver operating characteristic curves, calibration curves and decision curve analysis. Kaplan‒Meier survival analysis was used to assess the difference of the overall survival (OS) in the TDs-positive and TDs-negative groups. RESULTS The radiomics signature was composed of 11 TDs status related features. The AUCs of the radiomics model in the training cohort, internal validation and external validation cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated the radiomics signature and clinical independent predictors (CT-N, CEA and CA199) showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the training cohort, internal validation and external validation cohorts, respectively. The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor of OS. CONCLUSION The radiomics nomogram based on CT radiomics features and clinical independent predictors could effectively predict the preoperative TDs status and OS of colon cancer. IMPORTANT FINDINGS CT-based radiomics nomogram may be applied in the individual preoperative prediction of TDs status in colon cancer. Additionally, there was a significant difference in OS between the high-risk and low-risk groups defined by the radiomics nomogram, in which patients with high-risk TDs had a significantly worse OS, compared with those with low-risk TDs.
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Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Guodong Xu
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Yuwei Wang
- Department of Record room, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Hui Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361.
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [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: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Ungan G, Lavandier AF, Rouanet J, Hordonneau C, Chauveau B, Pereira B, Boyer L, Garcier JM, Mansard S, Bartoli A, Magnin B. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg 2022; 17:1867-1877. [PMID: 35650345 DOI: 10.1007/s11548-022-02662-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. METHODS We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. RESULTS The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001). CONCLUSION This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
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Affiliation(s)
- Gulnur Ungan
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Anne-Flore Lavandier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jacques Rouanet
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Benoit Chauveau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Bruno Pereira
- Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France
| | - Louis Boyer
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jean-Marc Garcier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France.,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Sandrine Mansard
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Benoit Magnin
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. .,Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France. .,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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