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Zhang X, Zhong M, Fu X, Pan H, Liu H, Chen J, Guo F. Exploring the oncogenic potential of Aiolos in lung cancer through OTUB1-mediated ubiquitination. Heliyon 2024; 10:e37710. [PMID: 39315162 PMCID: PMC11417157 DOI: 10.1016/j.heliyon.2024.e37710] [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: 06/09/2024] [Revised: 08/30/2024] [Accepted: 09/09/2024] [Indexed: 09/25/2024] Open
Abstract
Aiolos (IKZF3), a zinc finger transcription factor, has been identified in various solid tumors. While most research on Aiolos focuses on its role in the hematopoietic system, its expression patterns, mechanisms of action, and biological impacts in lung cancer remain relatively unexplored. This study investigates Aiolos' role in the proliferation, migration, and invasion of lung cancer cells. Our findings indicate that Aiolos overexpression enhances these cellular processes, suggesting its potential contribution to the advancement of the disease. However, the precise mechanisms underlying these effects require further investigation. Additionally, we identified OTUB1 as a potential Aiolos-interacting protein. OTUB1, a deubiquitinating enzyme, removes ubiquitin chains from target proteins, thereby affecting their stability, function, or localization. Our results suggest that OTUB1 specially bound to Aiolos and reduces its ubiquitination, which may influence Aiolos-related biological functions, including cell migration and invasion. This study highlights the pivotal roles of Aiolos and OTUB1 in lung cancer progression, potentially offering new therapeutic targets.
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Affiliation(s)
- Xiuwen Zhang
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Mei Zhong
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xinyue Fu
- The South China University of Technology School of Medicine, Guangzhou, 510006, China
| | - Hongli Pan
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hongyu Liu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jun Chen
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Fengjie Guo
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
- The South China University of Technology School of Medicine, Guangzhou, 510006, China
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Shi L, Zhao J, Wei Z, Wu H, Sheng M. Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 14:1381217. [PMID: 39381037 PMCID: PMC11458374 DOI: 10.3389/fonc.2024.1381217] [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: 02/09/2024] [Accepted: 09/05/2024] [Indexed: 10/10/2024] Open
Abstract
Objectives The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China
| | - Huiqun Wu
- Medical School, Nantong University, Nantong, China
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People’s Hospital, Nantong, China
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Priyadarshini K, Ali SA, Sivanandam K, Alagarsamy M. Human lung cancer classification and comprehensive analysis using different machine learning techniques. Microsc Res Tech 2024. [PMID: 39295255 DOI: 10.1002/jemt.24682] [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: 04/12/2024] [Revised: 06/14/2024] [Accepted: 08/16/2024] [Indexed: 09/21/2024]
Abstract
Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.
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Affiliation(s)
- K Priyadarshini
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
| | - S Ahamed Ali
- Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamilnadu, India
| | - K Sivanandam
- Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamilnadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Trichy, Tamilnadu, India
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Liu H, Lao M, Zhang Y, Chang C, Yin Y, Wang R. Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study. Front Oncol 2024; 14:1346336. [PMID: 39355130 PMCID: PMC11442173 DOI: 10.3389/fonc.2024.1346336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 08/27/2024] [Indexed: 10/03/2024] Open
Abstract
Purpose This study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC). Methods Pretreatment 18F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers. Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different model was assessed and compared using the DeLong test. Results A total of 227 patients with locally advanced cervical cancer were enrolled in this study (N=136 for the training cohort, N=59 for the internal validation cohort, and N=32 for the external validation cohort). The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% confidence interval [CI], 0.715-0.986) in the internal validation cohort, which were higher than those of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339-0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in either the training cohort (z=0.940, P=0.347) or the internal validation cohort (z=0.285, P=0.776). In the external validation cohort, the lightGBM-based PET radiomics model achieved good discrimination between SCC and AC (AUC = 0.730). Conclusions The lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.
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Affiliation(s)
- Huiling Liu
- Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
- Department of Radiation Oncology, Binzhou People’s Hospital, Binzhou, China
| | - Mi Lao
- Department of Cardiology, Binzhou People’s Hospital, Binzhou, China
| | - Yalin Zhang
- Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
| | - Cheng Chang
- Department of Nuclear Medicine, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Urumuqi, China
- Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region, Urumuqi, China
- Clinical Key Specialty of Radiotherapy of Xinjiang Uygur Autonomous Region, Urumuqi, China
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Wang K, Tu N, Feng H, Zhou Y, Bu L. Preoperative prognostic prediction for invasive pulmonary adenocarcinoma: Impact of 18F-FDG PET/CT semi-quantitative parameters associated with new histological subtype classification. Clin Radiol 2024:S0009-9260(24)00504-X. [PMID: 39341726 DOI: 10.1016/j.crad.2024.08.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/29/2024] [Accepted: 08/30/2024] [Indexed: 10/01/2024]
Abstract
AIMS To explore the preoperative predictive value of 18F-FDG PET/CT for poor prognostic histologic subtypes of invasive pulmonary adenocarcinoma (IPA) under new classification. MATERIALS AND METHODS This study included 316 patients. Histopathology of IPA was evaluated by recording the percentage of each histologic component. PET/CT parameters were compared among IPAs with different risks of recurrence. Optimum cutoff values of PET/CT parameters were calculated using ROC curve analysis. Overall survival (OS) and disease-free survival (DFS) were calculated using Kaplan-Meier method, and survival differences between groups were tested using log-rank test. Multivariate analysis for survival was performed using the Cox regression model. RESULTS Patients were divided into low (LRR), intermediate (IRR), and modified high (mHRR) risk of recurrence group incorporating typical (HRR-T) and nontypical (HRR-NT) subgroups based on histologic patterns. There were significant differences in SUVmax, SUVmean, SUVmin, SUVSD, TLG, and tumor size among three groups. HRR-NT had lower SUVmax, SUVmean, SUVmin, SUVSD and TLG than HRR-T subgroup, and higher SUVmax, SUVmean, SUVmin, SUVSD, MTV, TLG and tumor size than IRR group. ROC curve analysis showed that SUVmax had highest AUC (0.815) in distinguishing LRR and IRR. TLG had highest AUC (0.741) in distinguishing IRR and mHRR. Multivariable analysis showed that tumor size and SUVmax were independent predictors of DFS and OS. CONCLUSIONS High risk of recurrence of IPA exhibited higher 18F-FDG uptake and tumor size. Tumor size and SUVmax could be used as preoperative surrogates for the IASLC grading system. 18F-FDG PET/CT can improve the preoperative prognostic prediction for IPA patients.
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Affiliation(s)
- K Wang
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China
| | - N Tu
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China
| | - H Feng
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China
| | - Y Zhou
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China
| | - L Bu
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China.
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Wang L, Zhang C, Zhang Y, Li J. An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans. Bioengineering (Basel) 2024; 11:767. [PMID: 39199725 PMCID: PMC11351493 DOI: 10.3390/bioengineering11080767] [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: 06/20/2024] [Revised: 07/16/2024] [Accepted: 07/27/2024] [Indexed: 09/01/2024] Open
Abstract
When dealing with small targets in lung cancer detection, the YOLO V8 algorithm may encounter false positives and misses. To address this issue, this study proposes an enhanced YOLO V8 detection model. The model integrates a large separable kernel attention mechanism into the C2f module to expand the information retrieval range, strengthens the extraction of lung cancer features in the Backbone section, and achieves effective interaction between multi-scale features in the Neck section, thereby enhancing feature representation and robustness. Additionally, depth-wise convolution and Coordinate Attention mechanisms are embedded in the Fast Spatial Pyramid Pooling module to reduce feature loss and improve detection accuracy. This study introduces a Minimum Point Distance-based IOU loss to enhance correlation between predicted and ground truth bounding boxes, improving adaptability and accuracy in small target detection. Experimental validation demonstrates that the improved network outperforms other mainstream detection networks in terms of average precision values and surpasses other classification networks in terms of accuracy. These findings validate the outstanding performance of the enhanced model in the localization and recognition aspects of lung cancer auxiliary diagnosis.
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Affiliation(s)
| | | | | | - Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (L.W.); (C.Z.); (Y.Z.)
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Feng S, Zhang R, Zhang W, Yang Y, Song A, Chen J, Wang F, Xu J, Liang C, Liang X, Chen R, Liang Z. Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning. Respiration 2024:1-14. [PMID: 39047695 DOI: 10.1159/000540383] [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] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
INTRODUCTION Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features. METHODS We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results. RESULTS The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively. CONCLUSIONS DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.
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Affiliation(s)
- Shengchuan Feng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China,
| | - Ran Zhang
- Neusoft Medical Systems Co., Ltd., Shenyang, China
| | - Wenxiu Zhang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China
| | - Yuqiong Yang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Aiqi Song
- Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Jiawei Chen
- First Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Fengyan Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jiaxuan Xu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Cuixia Liang
- Neusoft Medical Systems Co., Ltd., Shenyang, China
| | - Xiaoyun Liang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Wang H, Qiu J, Lu W, Xie J, Ma J. Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images. Skeletal Radiol 2024:10.1007/s00256-024-04752-x. [PMID: 39028463 DOI: 10.1007/s00256-024-04752-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
OBJECTIVES This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach. MATERIALS AND METHODS We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC). RESULTS Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set. CONCLUSIONS Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.
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Affiliation(s)
- Huili Wang
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, 250012, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian, 271016, China
| | - Weizhao Lu
- School of Radiology, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian, 271016, China
| | - Jindong Xie
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, 250012, China.
| | - Junchi Ma
- School of Radiology, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian, 271016, 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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Dong H, Xi Y, Liu K, Chen L, Li Y, Pan X, Zhang X, Ye X, Ding Z. A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study. Eur J Radiol 2024; 176:111532. [PMID: 38820952 DOI: 10.1016/j.ejrad.2024.111532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/14/2024] [Accepted: 05/24/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVE To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.
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Affiliation(s)
- Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Yuzhen Xi
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yang Li
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xingwei Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - XiaoDan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China.
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
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11
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Liang B, Tong C, Nong J, Zhang Y. Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01152-4. [PMID: 38861072 DOI: 10.1007/s10278-024-01152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 06/12/2024]
Abstract
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer.
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Affiliation(s)
- Baoyu Liang
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
| | - Chao Tong
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
| | - Jingying Nong
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
| | - Yi Zhang
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
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12
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Kumar A, Vishwakarma A, Bajaj V. ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108207. [PMID: 38723437 DOI: 10.1016/j.cmpb.2024.108207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/20/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. METHODS Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. RESULTS The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. CONCLUSION The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.
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Affiliation(s)
- Anurodh Kumar
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Amit Vishwakarma
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Varun Bajaj
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India; Maulana Azad National Institute of Technology Bhopal, Bhopal, 462003, India.
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13
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Saha A, Ganie SM, Dutta Pramanik PK, Yadav RK, Mallik S, Zhao Z. Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:128. [PMID: 38822231 PMCID: PMC11140995 DOI: 10.1186/s12880-024-01315-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024] Open
Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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14
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Saha A, Ganie SM, Pramanik PKD, Yadav RK, Mallik S, Zhao Z. VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:120. [PMID: 38789925 PMCID: PMC11127393 DOI: 10.1186/s12880-024-01238-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/05/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. METHODS In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. RESULTS The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. CONCLUSION VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.
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Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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15
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Hinzpeter R, Mirshahvalad SA, Murad V, Avery L, Kulanthaivelu R, Kohan A, Ortega C, Elimova E, Yeung J, Hope A, Metser U, Veit-Haibach P. The [ 18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study. Cancers (Basel) 2024; 16:1873. [PMID: 38791955 PMCID: PMC11119256 DOI: 10.3390/cancers16101873] [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: 04/17/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
We aimed to investigate whether [18F]F-FDG-PET/CT-derived radiomics can classify histologic subtypes and determine the anatomical origin of various malignancies. In this IRB-approved retrospective study, 391 patients (age = 66.7 ± 11.2) with pulmonary (n = 142), gastroesophageal (n = 128) and head and neck (n = 121) malignancies were included. Image segmentation and feature extraction were performed semi-automatically. Two models (all possible subset regression [APS] and recursive partitioning) were employed to predict histology (squamous cell carcinoma [SCC; n = 219] vs. adenocarcinoma [AC; n = 172]), the anatomical origin, and histology plus anatomical origin. The recursive partitioning algorithm outperformed APS to determine histology (sensitivity 0.90 vs. 0.73; specificity 0.77 vs. 0.65). The recursive partitioning algorithm also revealed good predictive ability regarding anatomical origin. Particularly, pulmonary malignancies were identified with high accuracy (sensitivity 0.93; specificity 0.98). Finally, a model for the synchronous prediction of histology and anatomical disease origin resulted in high accuracy in determining gastroesophageal AC (sensitivity 0.88; specificity 0.92), pulmonary AC (sensitivity 0.89; specificity 0.88) and head and neck SCC (sensitivity 0.91; specificity 0.92). Adding PET-features was associated with marginal incremental value for both the prediction of histology and origin in the APS model. Overall, our study demonstrated a good predictive ability to determine patients' histology and anatomical origin using [18F]F-FDG-PET/CT-derived radiomics features, mainly from CT.
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Affiliation(s)
- Ricarda Hinzpeter
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Seyed Ali Mirshahvalad
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Vanessa Murad
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Lisa Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 1X6, Canada;
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Roshini Kulanthaivelu
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Andres Kohan
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Claudia Ortega
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Andrew Hope
- Department of Radiation Oncology, University Health Network, Toronto, ON M5G 2C4, Canada;
| | - Ur Metser
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Patrick Veit-Haibach
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
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16
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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [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: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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Jiang X, Li T, Wang J, Zhang Z, Chen X, Zhang J, Zhao X. Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study. Cancer Biother Radiopharm 2024; 39:169-177. [PMID: 38193811 DOI: 10.1089/cbr.2023.0162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.
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Affiliation(s)
- Xiaojing Jiang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Tianyue Li
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
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Wang H, Chen Y, Qiu J, Xie J, Lu W, Ma J, Jia M. Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients. Med Phys 2024; 51:2578-2588. [PMID: 37966123 DOI: 10.1002/mp.16839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Bone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination. PURPOSE Machine learning analysis of [99mTc]-methylene diphosphate (99mTc-MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer. METHODS One hundred forty-one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK-SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics-clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test. RESULTS The optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively. CONCLUSIONS The machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics-clinical integrated model demonstrated the best performance.
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Affiliation(s)
- Huili Wang
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yiru Chen
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Jindong Xie
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Weizhao Lu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Junchi Ma
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Mingsheng Jia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
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Lu W, Wu G, Miao X, Ma J, Wang Y, Xu H, Shentu D, Xue S, Xia Q, Wang Y, Wang L. The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy. Cancer Immunol Immunother 2024; 73:87. [PMID: 38554161 PMCID: PMC10981596 DOI: 10.1007/s00262-024-03644-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/29/2024] [Indexed: 04/01/2024]
Abstract
OBJECTIVE To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy. METHODS 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model. RESULT The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively. CONCLUSION The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.
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Affiliation(s)
- Wenxin Lu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Guangyu Wu
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Xianyuan Miao
- Department of Oncology, Ning Bo Hangzhou Bay Hospital, Ningbo, 315336, China
| | - Jingyu Ma
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yanling Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Haiyan Xu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Daiyuan Shentu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Shengbai Xue
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Qing Xia
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yu Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Liwei Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
- State Key Laboratory of Systems Medicine for Cancer of Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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20
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Wu J, Zhou Y, Xu C, Yang C, Liu B, Zhao L, Song J, Wang W, Yang Y, Liu N. Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer. BMC Cancer 2024; 24:170. [PMID: 38310283 PMCID: PMC10838455 DOI: 10.1186/s12885-024-11862-1] [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: 10/23/2023] [Accepted: 01/09/2024] [Indexed: 02/05/2024] Open
Abstract
BACKGROUND The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). METHODS Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis. RESULTS A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). CONCLUSION Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.
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Affiliation(s)
- Jiehan Wu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
- Langfang Health Vocational College, Siguang Road, Guangyang District, Langfang, 065000, Hebei, China
| | - Yuntao Zhou
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Chang Xu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Chengwen Yang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Bingxin Liu
- College of Arts and Sciences, Lehigh University, 27 Memorial Drive West, Bethlehem, PA, 18015, USA
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Jiawei Song
- Department of Oncology, the People's Hospital of Ganyu District, Lianyungang, 222100, China
| | - Wei Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Yining Yang
- The Department of Radiotherapy, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Ningbo Liu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
- Hetian District People's Hospital, Hetian, 848000, Xinjiang, China.
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21
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Alves VM, dos Santos Cardoso J, Gama J. Classification of Pulmonary Nodules in 2-[ 18F]FDG PET/CT Images with a 3D Convolutional Neural Network. Nucl Med Mol Imaging 2024; 58:9-24. [PMID: 38261899 PMCID: PMC10796312 DOI: 10.1007/s13139-023-00821-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/17/2023] [Accepted: 08/08/2023] [Indexed: 01/25/2024] Open
Abstract
Purpose 2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images. Methods One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used. Results The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives. Conclusion A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images. Supplementary Information The online version contains supplementary material available at 10.1007/s13139-023-00821-6.
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Affiliation(s)
- Victor Manuel Alves
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Department of Nuclear Medicine, University Hospital Center of São João, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Jaime dos Santos Cardoso
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - João Gama
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
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22
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Zhao H, Su Y, Lyu Z, Tian L, Xu P, Lin L, Han W, Fu P. Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning. Acad Radiol 2024; 31:35-45. [PMID: 37117141 DOI: 10.1016/j.acra.2023.03.032] [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: 02/06/2023] [Revised: 03/14/2023] [Accepted: 03/22/2023] [Indexed: 04/30/2023]
Abstract
RATIONALE AND OBJECTIVES To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) images, and to explore the potential value of DL technology. MATERIALS AND METHODS Preoperative 18F-FDG PET/CT images of 189 patients with NSCLC were retrospectively collected. The whole cohort was randomly divided into a training cohort, a validation cohort, and an internal/extended test cohort at the ratio of 6:2:2 after preprocessing the images. In the training and validation cohorts, seven DL models-Shufflenet, VGG16, Googlenet, Inception v3, Resnet50, Densenet201, and Mobilenet v2-were trained and optimized. The generalization ability and clinical utility of the optimal model were evaluated in the internal and extended test cohorts. Moreover, Spearman's correlation analysis was used to evaluate the correlation between DL features and traditional radiological features such as tumor size and maximum standardized uptake values (SUVmax). RESULTS Some DL features were significantly correlated with SUVmax and tumor size (P < 0.05). The Mobilenet v2 model achieved the best performance during the model development and validation phases. In the internal test group (area under the receiver operating characteristic curve [AUC]: 0.744, area under the precision-recall curve [AP]: 0.759) and extended test group (AUC: 0.767, AP: 0.768), the Mobilenet v2 model showed good generalization ability and reproducibility. Meanwhile, the decision curve analysis revealed that patients can benefit from the decisions made based on the Mobilenet v2 model. CONCLUSION DL models offer great potential for classifying NSCLC pathological subtypes. Specifically, the Mobilenet v2 model performs well at end-to-end non-invasive pathological subtype stratification of NSCLC.
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Affiliation(s)
- Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Xu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lin Lin
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wei Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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23
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Sun J, Cong C, Li X, Zhou W, Xia R, Liu H, Wang Y, Xu Z, Chen X. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 2024; 34:662-672. [PMID: 37535155 DOI: 10.1007/s00330-023-10003-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 05/08/2023] [Accepted: 06/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics. METHODS One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models. RESULTS The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model. CONCLUSIONS Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.
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Affiliation(s)
- Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Chao Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China
| | - Xinpeng Li
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | | | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Zhiqiang Xu
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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Kidera E, Koyasu S, Hirata K, Hamaji M, Nakamoto R, Nakamoto Y. Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET. Ann Nucl Med 2024; 38:71-80. [PMID: 37755604 DOI: 10.1007/s12149-023-01866-5] [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: 05/06/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE To develop a convolutional neural network (CNN)-based program to analyze maximum intensity projection (MIP) images of 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) positron emission tomography (PET) scans, aimed at predicting lymph node metastasis of non-small cell lung cancer (NSCLC), and to evaluate its effectiveness in providing diagnostic assistance to radiologists. METHODS We obtained PET images of NSCLC from public datasets, including those of 435 patients with available N-stage information, which were divided into a training set (n = 304) and a test set (n = 131). We generated 36 maximum intensity projection (MIP) images for each patient. A residual network (ResNet-50)-based CNN was trained using the MIP images of the training set to predict lymph node metastasis. Lymph node metastasis in the test set was predicted by the trained CNN as well as by seven radiologists twice: first without and second with CNN assistance. Diagnostic performance metrics, including accuracy and prediction error (the difference between the truth and the predictions), were calculated, and reading times were recorded. RESULTS In the test set, 67 (51%) patients exhibited lymph node metastases and the CNN yielded 0.748 predictive accuracy. With the assistance of the CNN, the prediction error was significantly reduced for six of the seven radiologists although the accuracy did not change significantly. The prediction time was significantly reduced for five of the seven radiologists with the median reduction ratio 38.0%. CONCLUSION The CNN-based program could potentially assist radiologists in predicting lymph node metastasis by increasing diagnostic confidence and reducing reading time without affecting diagnostic accuracy, at least in the limited situations using MIP images.
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Affiliation(s)
- Eitaro Kidera
- Department of Radiology, Kishiwada City Hospital, Kishiwada, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sho Koyasu
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Masatsugu Hamaji
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto University, Kyoto, Japan
| | - Ryusuke Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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25
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Nakajo M, Jinguji M, Ito S, Tani A, Hirahara M, Yoshiura T. Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Jpn J Radiol 2024; 42:28-55. [PMID: 37526865 PMCID: PMC10764437 DOI: 10.1007/s11604-023-01476-1] [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: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Megumi Jinguji
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Soichiro Ito
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atushi Tani
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuho Hirahara
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Tang X, Wu F, Chen X, Ye S, Ding Z. Current status and prospect of PET-related imaging radiomics in lung cancer. Front Oncol 2023; 13:1297674. [PMID: 38164195 PMCID: PMC10757959 DOI: 10.3389/fonc.2023.1297674] [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: 09/20/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Lung cancer is highly aggressive, which has a high mortality rate. Major types encompass lung adenocarcinoma, lung squamous cell carcinoma, lung adenosquamous carcinoma, small cell carcinoma, and large cell carcinoma. Lung adenocarcinoma and lung squamous cell carcinoma together account for more than 80% of cases. Diverse subtypes demand distinct treatment approaches. The application of precision medicine necessitates prompt and accurate evaluation of treatment effectiveness, contributing to the improvement of treatment strategies and outcomes. Medical imaging is crucial in the diagnosis and management of lung cancer, with techniques such as fluoroscopy, computed radiography (CR), digital radiography (DR), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and PET/MRI being essential tools. The surge of radiomics in recent times offers fresh promise for cancer diagnosis and treatment. In particular, PET/CT and PET/MRI radiomics, extensively studied in lung cancer research, have made advancements in diagnosing the disease, evaluating metastasis, predicting molecular subtypes, and forecasting patient prognosis. While conventional imaging methods continue to play a primary role in diagnosis and assessment, PET/CT and PET/MRI radiomics simultaneously provide detailed morphological and functional information. This has significant clinical potential value, offering advantages for lung cancer diagnosis and treatment. Hence, this manuscript provides a review of the latest developments in PET-related radiomics for lung cancer.
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Affiliation(s)
- Xin Tang
- Department of Radiology, Hangzhou Wuyunshan Hospital (Hangzhou Health Promotion Research Institute), Hangzhou, China
| | - Fan Wu
- Department of Nuclear Medicine and Radiology, Shulan Hangzhou Hospital affiliated to Shulan International Medical College of Zhejiang Shuren University, Hangzhou, China
| | - Xiaofen Chen
- Department of Radiology, Hangzhou Wuyunshan Hospital (Hangzhou Health Promotion Research Institute), Hangzhou, China
| | - Shengli Ye
- Department of Nuclear Medicine and Radiology, Shulan Hangzhou Hospital affiliated to Shulan International Medical College of Zhejiang Shuren University, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People’s Hospital, Hangzhou, China
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Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
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Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
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Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Müller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph 2023; 110:102310. [PMID: 37979340 DOI: 10.1016/j.compmedimag.2023.102310] [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] [Revised: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Nicola Falcionelli
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Paolo Sernani
- Department of Law, University of Macerata (UNIMC), Macerata, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Aldo Franco Dragoni
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy.
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Jimenez-Mesa C, Arco JE, Martinez-Murcia FJ, Suckling J, Ramirez J, Gorriz JM. Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects. Pharmacol Res 2023; 197:106984. [PMID: 37940064 DOI: 10.1016/j.phrs.2023.106984] [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: 07/24/2023] [Revised: 10/04/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve into the transformative impact of ML and DL in this domain. Firstly, a brief analysis is provided of how these algorithms have evolved and which are the most widely applied in this domain. Their different potential applications in nuclear imaging are then discussed, such as optimization of image adquisition or reconstruction, biomarkers identification, multimodal fusion and the development of diagnostic, prognostic, and disease progression evaluation systems. This is because they are able to analyse complex patterns and relationships within imaging data, as well as extracting quantitative and objective measures. Furthermore, we discuss the challenges in implementation, such as data standardization and limited sample sizes, and explore the clinical opportunities and future horizons, including data augmentation and explainable AI. Together, these factors are propelling the continuous advancement of more robust, transparent, and reliable systems.
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Affiliation(s)
- Carmen Jimenez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Communications Engineering, University of Malaga, 29010, Spain
| | | | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK.
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Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
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Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Cui N, Li J, Jiang Z, Long Z, Liu W, Yao H, Li M, Li W, Wang K. Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma. Ann Nucl Med 2023; 37:605-617. [PMID: 37598412 DOI: 10.1007/s12149-023-01861-w] [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: 06/12/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVES This study aimed to establish a radiomics model based on 18F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively. METHODS We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with 18F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts. RESULTS 165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739). CONCLUSIONS A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.
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Affiliation(s)
- Nan Cui
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Jiatong Li
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Zhiyun Jiang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Zhiping Long
- Department of Epidemiology, School of Public Health, Harbin Medical University, 157 Baojian Road, Harbin, 150081, Heilongjiang, China
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Hongyang Yao
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Mingshan Li
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Wei Li
- Interventional Vascular Surgery Department, The 4th Affiliated Hospital of Harbin Medical University, Harbin Medical University, 37 Yiyuan Road, Harbin, 150001, Heilongjiang, China
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
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Shu Y, Xu W, Su R, Ran P, Liu L, Zhang Z, Zhao J, Chao Z, Fu G. Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis. Front Immunol 2023; 14:1251645. [PMID: 37799725 PMCID: PMC10547882 DOI: 10.3389/fimmu.2023.1251645] [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: 07/02/2023] [Accepted: 08/24/2023] [Indexed: 10/07/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.
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Affiliation(s)
- Yang Shu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Wei Xu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Rui Su
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Pancen Ran
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Lei Liu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhizhao Zhang
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jing Zhao
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Guobin Fu
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Department of Oncology, The Third Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China
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Zheng Z, Yao H, Lin C, Huang K, Chen L, Shao Z, Zhou H, Zhao G. KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections. Front Genet 2023; 14:1254435. [PMID: 37790704 PMCID: PMC10544998 DOI: 10.3389/fgene.2023.1254435] [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: 07/07/2023] [Accepted: 08/10/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions. Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training. Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification methods.
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Affiliation(s)
- Zhaoliang Zheng
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Henian Yao
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chengchuang Lin
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Kaixin Huang
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Luoxuan Chen
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Ziling Shao
- Jinan University-University of Birmingham Joint Institute at Jinan University, Guangdong, China
| | - Haiyu Zhou
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Gansen Zhao
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
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Chu X, Niu L, Yang X, He S, Li A, Chen L, Liang Z, Jing D, Zhou R. Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial. iScience 2023; 26:107634. [PMID: 37664612 PMCID: PMC10474462 DOI: 10.1016/j.isci.2023.107634] [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: 03/31/2023] [Revised: 04/07/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.
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Affiliation(s)
- Xianjing Chu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Lishui Niu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xianghui Yang
- Department of Oncology, Changsha Central Hospital, Changsha 410004, China
| | - Shiqi He
- Department of Computer Science, University of British Columbia, 2329 West Mall, Vancouver, British Columbia, Canada
| | - Aixin Li
- Department of Radiotherapy, The First Affiliated Hospital, Hengyang Medical School, University of South, Hengyang 421001, China
| | - Liu Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhan Liang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Rongrong Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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Shao J, Feng J, Li J, Liang S, Li W, Wang C. Novel tools for early diagnosis and precision treatment based on artificial intelligence. CHINESE MEDICAL JOURNAL PULMONARY AND CRITICAL CARE MEDICINE 2023; 1:148-160. [PMID: 39171128 PMCID: PMC11332840 DOI: 10.1016/j.pccm.2023.05.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 08/23/2024]
Abstract
Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.
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Affiliation(s)
- Jun Shao
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaming Feng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jingwei Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shufan Liang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Pei G, Wang D, Sun K, Yang Y, Tang W, Sun Y, Yin S, Liu Q, Wang S, Huang Y. Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study. Front Oncol 2023; 13:1224455. [PMID: 37546407 PMCID: PMC10400286 DOI: 10.3389/fonc.2023.1224455] [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: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Background Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Methods Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Results The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. Conclusions DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.
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Affiliation(s)
- Guotian Pei
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Kunkun Sun
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Yingshun Yang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Wen Tang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Yanfeng Sun
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Siyuan Yin
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Qiang Liu
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Shuai Wang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Yuqing Huang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
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Xiao H, Liu Q, Li L. MFMANet: Multi-feature Multi-attention Network for efficient subtype classification on non-small cell lung cancer CT images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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Lin J, Yu Y, Zhang X, Wang Z, Li S. Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images. J Digit Imaging 2023; 36:1029-1037. [PMID: 36828962 PMCID: PMC10287608 DOI: 10.1007/s10278-023-00792-2] [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: 08/16/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/26/2023] Open
Abstract
Non-invasive diagnostic method based on radiomic features in patients with non-small cell lung cancer (NSCLC) has attracted attention. This study aimed to develop a CT image-based model for both histological typing and clinical staging of patients with NSCLC. A total of 309 NSCLC patients with 537 CT series from The Cancer Imaging Archive (TCIA) database were included in this study. All patients were randomly divided into the training set (247 patients, 425 CT series) and testing set (62 patients, 112 CT series). A total of 107 radiomic features were extracted. Four classifiers including random forest, XGBoost, support vector machine, and logistic regression were used to construct the classification model. The classification model had two output layers: histological type (adenocarcinoma, squamous cell carcinoma, and large cell) and clinical stage (I, II, and III) of NSCLC patients. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence interval (CI) were utilized to evaluate the performance of the model. Seven features were selected for inclusion in the classification model. The random forest model had the best classification ability compared with other classifiers. The AUC of the RF model for histological typing and clinical staging of NSCLC patients in the testing set was 0.700 (95% CI, 0.641-0.759) and 0.881 (95% CI, 0.842-0.920), respectively. The CT image-based radiomic feature model had good classification ability for both histological typing and clinical staging of patients with NSCLC.
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Affiliation(s)
- Jing Lin
- Department of Medical Imaging, Shanghai Electric Power Hospital, Changning District, No. 937 Yan'an West Road, Shanghai, 20050, China.
| | - Yunjie Yu
- Department of Medical Imaging, Shanghai Electric Power Hospital, Changning District, No. 937 Yan'an West Road, Shanghai, 20050, China
| | - Xianlong Zhang
- Department of Medical Imaging, Shanghai Electric Power Hospital, Changning District, No. 937 Yan'an West Road, Shanghai, 20050, China
| | - Zhenglei Wang
- Department of Medical Imaging, Shanghai Electric Power Hospital, Changning District, No. 937 Yan'an West Road, Shanghai, 20050, China
| | - Shujuan Li
- Department of Medical Imaging, Shanghai Electric Power Hospital, Changning District, No. 937 Yan'an West Road, Shanghai, 20050, China
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Chen K, Wang M, Song Z. Multi-task learning-based histologic subtype classification of non-small cell lung cancer. LA RADIOLOGIA MEDICA 2023; 128:537-543. [PMID: 36976403 DOI: 10.1007/s11547-023-01621-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
PURPOSE In clinical applications, accurate histologic subtype classification of lung cancer is important for determining appropriate treatment plans. The purpose of this paper is to evaluate the role of multi-task learning in the classification of adenocarcinoma and squamous cell carcinoma. MATERIAL AND METHODS In this paper, we propose a novel multi-task learning model for histologic subtype classification of non-small cell lung cancer based on computed tomography (CT) images. The model consists of a histologic subtype classification branch and a staging branch, which share a part of the feature extraction layers and are simultaneously trained. By optimizing on the two tasks simultaneously, our model could achieve high accuracy in histologic subtype classification of non-small cell lung cancer without relying on physician's precise labeling of tumor areas. In this study, 402 cases from The Cancer Imaging Archive (TCIA) were used in total, and they were split into training set (n = 258), internal test set (n = 66) and external test set (n = 78). RESULTS Compared with the radiomics method and single-task networks, our multi-task model could reach an AUC of 0.843 and 0.732 on internal and external test set, respectively. In addition, multi-task network can achieve higher accuracy and specificity than single-task network. CONCLUSION Compared with the radiomics methods and single-task networks, our multi-task learning model could improve the accuracy of histologic subtype classification of non-small cell lung cancer by sharing network layers, which no longer relies on the physician's precise labeling of lesion regions and could further reduce the manual workload of physicians.
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Affiliation(s)
- Kun Chen
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Manning Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China.
| | - Zhijian Song
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China.
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Pasini G, Stefano A, Russo G, Comelli A, Marinozzi F, Bini F. Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics? Diagnostics (Basel) 2023; 13:1167. [PMID: 36980475 PMCID: PMC10046953 DOI: 10.3390/diagnostics13061167] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.
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Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
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Ma X, Xia L, Chen J, Wan W, Zhou W. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model. Eur Radiol 2023; 33:1949-1962. [PMID: 36169691 DOI: 10.1007/s00330-022-09153-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma. METHODS A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test. RESULTS The proposed DL signature yielded an AUC of 0.948-0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature. CONCLUSIONS The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options. KEY POINTS • Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948-0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.
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Affiliation(s)
- Xiaoling Ma
- Medical Imaging Center, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China.
| | | | - Weijia Wan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Wen Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China
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Huang J, He W, Xu H, Yang S, Dai J, Guo W, Zeng M. Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:8964676. [PMID: 36794098 PMCID: PMC9925238 DOI: 10.1155/2023/8964676] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/07/2022] [Accepted: 01/21/2023] [Indexed: 02/08/2023]
Abstract
Lung cancer is the leading cause of cancer-related death in many countries, and an accurate histopathological diagnosis is of great importance in subsequent treatment. The aim of this study was to establish the random forest (RF) model based on radiomic features to automatically classify and predict lung adenocarcinoma (ADC), lung squamous cell carcinoma (SCC), and small cell lung cancer (SCLC) on unenhanced computed tomography (CT) images. Eight hundred and fifty-two patients (mean age: 61.4, range: 29-87, male/female: 536/316) with preoperative unenhanced CT and postoperative histopathologically confirmed primary lung cancers, including 525 patients with ADC, 161 patients with SCC, and 166 patients with SCLC, were included in this retrospective study. Radiomic features were extracted, selected, and then used to establish the RF classification model to analyse and classify primary lung cancers into three subtypes, including ADC, SCC, and SCLC according to histopathological results. The training (446 ADC, 137 SCC, and 141 SCLC) and testing cohorts (79 ADC, 24 SCC, and 25 SCLC) accounted for 85% and 15% of the whole datasets, respectively. The prediction performance of the RF classification model was evaluated by F1 scores and the receiver operating characteristic (ROC) curve. On the testing cohort, the areas under the ROC curve (AUC) of the RF model in classifying ADC, SCC, and SCLC were 0.74, 0.77, and 0.88, respectively. The F1 scores achieved 0.80, 0.40, and 0.73 in ADC, SCC, and SCLC, respectively, and the weighted average F1 score was 0.71. In addition, for the RF classification model, the precisions were 0.72, 0.64, and 0.70; the recalls were 0.86, 0.29, and 0.76; and the specificities were 0.55, 0.96, and 0.92 in ADC, SCC, and SCLC. The primary lung cancers were feasibly and effectively classified into ADC, SCC, and SCLC based on the combination of RF classification model and radiomic features, which has the potential for noninvasive predicting histological subtypes of primary lung cancers.
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Affiliation(s)
- Jianfeng Huang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Wei He
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Haijia Xu
- School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Shan Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiajun Dai
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Weifeng Guo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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Cui X, Zheng S, Zhang W, Fan S, Wang J, Song F, Liu X, Zhu W, Ye Z. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT. Eur Radiol 2023:10.1007/s00330-023-09432-3. [PMID: 36723725 DOI: 10.1007/s00330-023-09432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 02/02/2023]
Abstract
OBJECTIVES This study aimed to develop and validate a predicting model for the histologic classification of solid lung lesions based on preoperative contrast-enhanced CT. METHODS A primary dataset of 1012 patients from Tianjin Medical University Cancer Institute and Hospital (TMUCIH) was randomly divided into a development cohort (708) and an internal validation cohort (304). Patients from the Second Hospital of Shanxi Medical University (SHSMU) were set as an external validation cohort (212). Two clinical factors (age, gender) and twenty-one characteristics on contrast-enhanced CT were used to construct a multinomial multivariable logistic regression model for the classification of seven common histologic types of solid lung lesions. The area under the receiver operating characteristic curve was used to assess the diagnostic performance of the model in the development and validation cohorts, separately. RESULTS Multivariable analysis showed that two clinical factors and twenty-one characteristics on contrast-enhanced CT were predictive in lung lesion histologic classification. The mean AUC of the proposed model for histologic classification was 0.95, 0.94, and 0.92 in the development, internal validation, and external validation cohort, respectively. When determining the malignancy of lung lesions based on histologic types, the mean AUC of the model was 0.88, 0.86, and 0.90 in three cohorts. CONCLUSIONS We demonstrated that by utilizing both clinical and CT characteristics on contrast-enhanced CT images, the proposed model could not only effectively stratify histologic types of solid lung lesions, but also enabled accurate assessment of lung lesion malignancy. Such a model has the potential to avoid unnecessary surgery for patients and to guide clinical decision-making for preoperative treatment. KEY POINTS • Clinical and CT characteristics on contrast-enhanced CT could be used to differentiate histologic types of solid lung lesions. • Predicting models using preoperative contrast-enhanced CT could accurately assessment of tumor malignancy based on predicted histologic types.
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Affiliation(s)
- Xiaonan Cui
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Sunyi Zheng
- Artificial Intelligence and Biomedical Image Analysis Lab, School of Engineering, Westlake University, Hangzhou, People's Republic of China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, People's Republic of China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Shuxuan Fan
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, People's Republic of China
| | - Feipeng Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Xu Liu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, People's Republic of China
| | - Weijie Zhu
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China
| | - Zhaoxiang Ye
- Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin , Tianjin, 300060, People's Republic of China.
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Dwivedi K, Rajpal A, Rajpal S, Agarwal M, Kumar V, Kumar N. An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification. Comput Biol Med 2023; 153:106544. [PMID: 36652866 DOI: 10.1016/j.compbiomed.2023.106544] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/17/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the molecular level that aids in distinguishing between its two prominent subtypes - Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). This paper proposes a novel explainable AI (XAI)-based deep learning framework to discover a small set of NSCLC biomarkers. The proposed framework comprises three modules - an autoencoder to shrink the input feature space, a feed-forward neural network to classify NSCLC instances into LUAD and LUSC, and a biomarker discovery module that leverages the combined network comprising the autoencoder and the feed-forward neural network. In the biomarker discovery module, XAI methods uncovered a set of 52 relevant biomarkers for NSCLC subtype classification. To evaluate the classification performance of the discovered biomarkers, multiple machine-learning models are constructed using these biomarkers. Using 10-Fold cross-validation, Multilayer Perceptron achieved an accuracy of 95.74% (±1.27) at 95% confidence interval. Further, using Drug-Gene Interaction Database, we observe that 14 of the discovered biomarkers are druggable. In addition, 28 biomarkers aid the prediction of the survivability of the patients. Out of 52 discovered biomarkers, we find that 45 biomarkers have been reported in previous studies on distinguishing between the two NSCLC subtypes. To the best of our knowledge, the remaining seven biomarkers have not yet been reported for NSCLC subtyping and could be further explored for their contribution to targeted therapy of lung cancer.
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Affiliation(s)
- Kountay Dwivedi
- Department of Computer Science, University of Delhi, Delhi, India.
| | - Ankit Rajpal
- Department of Computer Science, University of Delhi, Delhi, India.
| | | | | | - Virendra Kumar
- Department of Nuclear Magnetic Resonance Imaging, All India Institute of Medical Sciences, New Delhi, India.
| | - Naveen Kumar
- Department of Computer Science, University of Delhi, Delhi, India.
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Karthika K, Jothilakshmi GR. An early prediction of lung cancer, solid, liquid and semi-liquid deposition and its classification through measurement of physical characteristics using CT scan images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2163538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- K. Karthika
- Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India
| | - G. R. Jothilakshmi
- Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India
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Hu Q, Li K, Yang C, Wang Y, Huang R, Gu M, Xiao Y, Huang Y, Chen L. The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges. Front Oncol 2023; 13:1133164. [PMID: 36959810 PMCID: PMC10028142 DOI: 10.3389/fonc.2023.1133164] [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: 12/28/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC). Materials and methods A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis. Results Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability. Conclusion AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.
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Affiliation(s)
- Qiuyuan Hu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Ke Li
- Department of Cancer Biotherapy Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Conghui Yang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yue Wang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Rong Huang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Mingqiu Gu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yuqiang Xiao
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yunchao Huang
- Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
| | - Long Chen
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
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Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7793533. [PMID: 36561373 PMCID: PMC9767733 DOI: 10.1155/2022/7793533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/18/2022] [Accepted: 11/26/2022] [Indexed: 12/15/2022]
Abstract
The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.
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Hao P, Deng BY, Huang CT, Xu J, Zhou F, Liu ZX, Zhou W, Xu YK. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images. Front Oncol 2022; 12:994285. [PMID: 36338735 PMCID: PMC9630325 DOI: 10.3389/fonc.2022.994285] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/26/2022] [Indexed: 12/01/2023] Open
Abstract
PURPOSE To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features. METHOD AND MATERIALS This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves. RESULTS The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort. CONCLUSION Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.
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Affiliation(s)
- Peng Hao
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bo-Yu Deng
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chan-Tao Huang
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jun Xu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Fang Zhou
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhe-Xing Liu
- School of Biomedical Engineering, Southern Medical Uinversity, Guangzhou, China
| | - Wu Zhou
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi-Kai Xu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
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State of the Art: Lung Cancer Staging Using Updated Imaging Modalities. Bioengineering (Basel) 2022; 9:bioengineering9100493. [PMID: 36290461 PMCID: PMC9598500 DOI: 10.3390/bioengineering9100493] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022] Open
Abstract
Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists’ skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.
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50
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Zahedi S, Carvalho AS, Ejtehadifar M, Beck HC, Rei N, Luis A, Borralho P, Bugalho A, Matthiesen R. Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort. Cancers (Basel) 2022; 14:cancers14184366. [PMID: 36139528 PMCID: PMC9496668 DOI: 10.3390/cancers14184366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival. Abstract Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10−6).
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Affiliation(s)
- Sara Zahedi
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
| | - Ana Sofia Carvalho
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
| | - Mostafa Ejtehadifar
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
| | - Hans C. Beck
- Department of Clinical Biochemistry, Odense University Hospital, 5000 Odense, Denmark
| | - Nádia Rei
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
| | - Ana Luis
- Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal
| | - Paula Borralho
- Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal
| | - António Bugalho
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
- Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal
- Correspondence: (A.B.); (R.M.)
| | - Rune Matthiesen
- iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal
- Correspondence: (A.B.); (R.M.)
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