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Zhang J, Zhang Q, Zhao B, Shi G. Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients. Abdom Radiol (NY) 2024:10.1007/s00261-024-04331-7. [PMID: 38796795 DOI: 10.1007/s00261-024-04331-7] [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: 03/07/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024]
Abstract
PURPOSE Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. METHODS This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA). RESULTS The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793-0.893)], 0.802 (95% CI 0.688-0.889), and 0.751 (95% CI 0.652-0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756-0.895), 0.541 (95% CI 0.329-0.740), and 0.556 (95% CI 0.376-0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models. CONCLUSION This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.
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Affiliation(s)
- Jingjing Zhang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Qiang Zhang
- Department of Radiation Oncology, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Bo Zhao
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
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Adili D, Mohetaer A, Zhang W. Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis. Eur J Radiol 2024; 173:111249. [PMID: 38382422 DOI: 10.1016/j.ejrad.2023.111249] [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: 07/24/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, a lingering debate persists regarding the accuracy of these predictions. Against this backdrop, this study was conducted to examine the accuracy of radiomics in predicting the response to neoadjuvant chemotherapy in GC patients. METHODS An exhaustive search of relevant studies was conducted in PubMed, Cochrane, Embase, and Web of Science databases up to February 21, 2023. The radiomics quality scoring (RQS) tool was employed to assess study quality. Tumor response to neoadjuvant chemotherapy and survival outcomes were examined as outcome measures. RESULTS Fourteen studies involving 3,373 GC patients who had received neoadjuvant chemotherapy were incorporated in our meta-analysis. The mean RQS score across all studies was 36.3%, ranging between 0 and 63.9%. On the validation cohort, when the modeling variables were restricted to radiomic features alone, the predictive performance was characterized by a c-index of 0.750 (95% CI: 0.710-0.790), with a sensitivity of 0.67 (95% CI: 0.58-0.75) and a specificity of 0.77 (95% CI: 0.69-0.84) for the prediction of neoadjuvant chemotherapy response. When clinical data was integrated with radiomic features as modeling variables, the predictive performance improved, yielding a c-index of 0.814 (95% CI: 0.780-0.847), a sensitivity of 0.78 [95% CI: 0.70-0.84], and a specificity of 0.73 [95% CI: 0.67-0.79]. CONCLUSIONS Radiomics holds promise to noninvasively predict neoadjuvant chemotherapy response and survival outcomes among patients with locally advanced GC. Additionally, we underscore the need for future multicenter studies and the development of imaging-sourced tools for risk stratification in this patient population.
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Affiliation(s)
- Diliyaer Adili
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
| | - Aibibai Mohetaer
- Department of Cardiology, The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063 China
| | - Wenbin Zhang
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
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Tan Y, Feng LJ, Huang YH, Xue JW, Feng ZB, Long LL. Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer. BMC Cancer 2024; 24:368. [PMID: 38519974 PMCID: PMC10960497 DOI: 10.1186/s12885-024-12021-2] [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: 12/01/2023] [Accepted: 02/18/2024] [Indexed: 03/25/2024] Open
Abstract
OBJECTIVE This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.
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Affiliation(s)
- Yang Tan
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Li-Juan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Ying-He Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Jia-Wen Xue
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zhen-Bo Feng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China.
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Yimit Y, Yasin P, Tuersun A, Wang J, Wang X, Huang C, Abudoubari S, Chen X, Ibrahim I, Nijiati P, Wang Y, Zou X, Nijiati M. Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study. Acad Radiol 2024:S1076-6332(24)00131-4. [PMID: 38508934 DOI: 10.1016/j.acra.2024.02.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/22/2024]
Abstract
RATIONALE AND OBJECTIVES Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. MATERIALS AND METHODS Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. RESULTS 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. CONCLUSION The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.
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Affiliation(s)
- Yasen Yimit
- Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000
| | - Parhat Yasin
- Department of Spine Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054
| | - Abudouresuli Tuersun
- Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000
| | - Jingru Wang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080
| | - Xiaohong Wang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, 510630
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080
| | - Saimaitikari Abudoubari
- Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000
| | - Xingzhi Chen
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080
| | - Irshat Ibrahim
- Department of General Surgery, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000
| | - Pahatijiang Nijiati
- Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000
| | - Yunling Wang
- Department of Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054
| | - Xiaoguang Zou
- Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000; Clinical Medical Research Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000
| | - Mayidili Nijiati
- Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000.
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Xiang YH, Mou H, Qu B, Sun HR. Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study. World J Gastrointest Surg 2024; 16:345-356. [PMID: 38463348 PMCID: PMC10921214 DOI: 10.4240/wjgs.v16.i2.345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/01/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis. AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics. METHODS This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors. RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05). CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
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Affiliation(s)
- Ying-Hao Xiang
- Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China
| | - Huan Mou
- Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China
| | - Bo Qu
- Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China
| | - Hui-Rong Sun
- Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China
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Wang Y, Ye J, Zhou K, Chen N, Huang G, Feng G, Zhang G, Gou X. Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-023-00910-0. [PMID: 38376584 DOI: 10.1007/s10278-023-00910-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/13/2023] [Accepted: 08/23/2023] [Indexed: 02/21/2024]
Abstract
Forkhead box P3 (FOXP3) has been identified as a novel molecular marker in various types of cancer. The present study assessed the expression of FOXP3 in patients with head and neck squamous cell carcinoma (HNSCC) and its potential as a clinical prognostic indicator, and developed a radiomics model based on enhanced computed tomography (CT) imaging. Data from 483 patients with HNSCC were downloaded from the Cancer Genome Atlas for FOXP3 prognostic analysis and enhanced CT images from 139 patients included in the Cancer Imaging Archives, which were subjected to the maximum relevance and minimum redundancy and recursive feature elimination algorithms for radiomics feature extraction and processing. Logistic regression was used to build a model for predicting FOXP3 expression. A prognostic scoring system for radiomics score (RS), FOXP3, and patient clinicopathological factors was established to predict patient survival. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration curve and decision curve analysis (DCA) were used to evaluate model performance. Furthermore, the relationship between FOXP3 and the immune microenvironment, as well as the association between RS and immune checkpoint-related genes, was analyzed. Results of analysis revealed that patients with HNSCC and high FOXP3 mRNA expression exhibited better overall survival. Immune infiltration analysis revealed that FOXP3 had a positive correlation with CD4 + and CD8 + T cells and other immune cells. The 8 best radiomics features were selected to construct the radiomics model. In the FOXP3 expression prediction model, the AUC values were 0.707 and 0.702 for the training and validation sets, respectively. Additionally, the calibration curve and DCA demonstrated the positive diagnostic utility of the model. RS was correlated with immune checkpoint-related genes such as ICOS, CTLA4, and PDCD1. A predictive nomogram was established, the AUCs were 0.87, 0.787, and 0.801 at 12, 24, and 36 months, respectively, and DCA demonstrated the high clinical applicability of the nomogram. The enhanced CT radiomics model can predict expression of FOXP3 and prognosis in patients with HNSCC. As such, FOXP3 may be used as a novel prognostic marker to improve individualized clinical diagnosis and treatment decisions.
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Affiliation(s)
- Yi Wang
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Juan Ye
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Kai Zhou
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Nian Chen
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Gang Huang
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Guangyong Feng
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Guihai Zhang
- Department of Oncology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China.
| | - Xiaoxia Gou
- Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
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Tan Y, Feng LJ, Huang YH, Xue JW, Long LL, Feng ZB. A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features. Transl Oncol 2024; 40:101864. [PMID: 38141376 PMCID: PMC10788295 DOI: 10.1016/j.tranon.2023.101864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 12/25/2023] Open
Abstract
OBJECTIVE This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). METHODS Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. RESULTS From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. CONCLUSION This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.
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Affiliation(s)
- Yang Tan
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Li-Juan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Ying-He Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, PR China
| | - Jia-Wen Xue
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, PR China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, PR China.
| | - Zhen-Bo Feng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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Yan H, Huang G, Yang Z, Chen Y, Xiang Z. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:230-246. [PMID: 38343218 DOI: 10.1007/s10278-023-00906-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/12/2023] [Accepted: 09/11/2023] [Indexed: 03/02/2024]
Abstract
Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.
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Affiliation(s)
- Haowen Yan
- The First Affiliated Hospital of Jinan University, 510632, Guangzhou, China
- Department of Oncology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, China
| | - Gaoting Huang
- Department of Gynecology and Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, 510095, Guangzhou, China
| | - Zhihe Yang
- Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, China
| | - Yirong Chen
- Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, China.
- Jinan University, 601# Huangpu Avenue West, Tianhe District, 510632, Guangzhou, China.
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Vogele D, Mueller T, Wolf D, Otto S, Manoj S, Goetz M, Ettrich TJ, Beer M. Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors. Diagnostics (Basel) 2024; 14:198. [PMID: 38248074 PMCID: PMC10814393 DOI: 10.3390/diagnostics14020198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/10/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
PURPOSE Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the impact of sarcopenia on the prognosis of patients diagnosed with esophageal or gastric cancer. Additionally, it explores the significance of CT radiomics in both diagnostic and prognostic methodologies. MATERIALS AND METHODS CT scans of 83 patients with esophageal or gastric cancer taken at the time of diagnosis and during a follow-up period of one year were evaluated retrospectively. A total of 330 CT scans were analyzed. Seventy three of these patients received operative tumor resection after neoadjuvant chemotherapy, and 74% of the patients were male. The mean age was 64 years (31-83 years). Three time points (t) were defined as a basis for the statistical analysis in order to structure the course of the disease: t1 = initial diagnosis, t2 = following (neoadjuvant) chemotherapy and t3 = end of the first year after surgery in the "surgery" group or end of the first year after chemotherapy. Sarcopenia was determined using the psoas muscle index (PMI). The additional analysis included the analysis of selected radiomic features of the psoas major, quadratus lumborum, and erector spinae muscles at the L3 level. Disease progression was monitored according to the response evaluation criteria in solid tumors (RECIST 1.1). CT scans and radiomics were used to assess the likelihood of tumor progression and their correlation to sarcopenia. For machine learning, the established algorithms decision tree (DT), K-nearest neighbor (KNN), and random forest (RF) were applied. To evaluate the performance of each model, a 10-fold cross-validation as well as a calculation of Accuracy and Area Under the Curve (AUC) was used. RESULTS During the observation period of the study, there was a significant decrease in PMI. This was most evident in patients with surgical therapy in the comparison between diagnosis and after both neoadjuvant therapy and surgery (each p < 0.001). Tumor progression (PD) was not observed significantly more often in the patients with sarcopenia compared to those without sarcopenia at any time point (p = 0.277 to p = 0.465). On average, PD occurred after 271.69 ± 104.20 days. The time from initial diagnosis to PD in patients "with sarcopenia" was not significantly shorter than in patients "without sarcopenia" at any of the time points (p = 0.521 to p = 0.817). The CT radiomics of skeletal muscle could predict both sarcopenia and tumor progression, with the best results for the psoas major muscle using the RF algorithm. For the detection of sarcopenia, the Accuracy was 0.90 ± 0.03 and AUC was 0.96 ± 0.02. For the prediction of PD, the Accuracy was 0.88 ± 0.04 and the AUC was 0.93 ± 0.04. CONCLUSIONS In the present study, the CT radiomics of skeletal muscle together with machine learning correlated with the presence of sarcopenia, and this can additionally assist in predicting disease progression. These features can be classified as promising alternatives to conventional methods, with great potential for further research and future clinical application. However, when sarcopenia was diagnosed with PMI, no significant correlation between sarcopenia and PD could be observed.
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Affiliation(s)
- Daniel Vogele
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Teresa Mueller
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- Visual Computing Group, Institute for Media Informatics, Ulm University, 89081 Ulm, Germany
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Stephanie Otto
- Comprehensive Cancer Center Ulm (CCCU), Ulm University Medical Center, 89081 Ulm, Germany;
| | - Sabitha Manoj
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Michael Goetz
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Thomas J. Ettrich
- Department of Internal Medicine I, Ulm University Medical Center, 89081 Ulm, Germany;
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
- MoMan—Center for Translational Imaging, Department of Internal Medicine II, University Hospital of Ulm, 89081 Ulm, Germany
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Zhang M, Wang Y, Lv M, Sang L, Wang X, Yu Z, Yang Z, Wang Z, Sang L. Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis. Technol Cancer Res Treat 2024; 23:15330338241235769. [PMID: 38465611 DOI: 10.1177/15330338241235769] [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: 03/12/2024] Open
Abstract
Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.
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Affiliation(s)
- Minghui Zhang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Yan Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Mutian Lv
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Li Sang
- Department of Acupuncture and Massage, Shouguang Hospital of Traditional Chinese Medicine, Weifang, P. R. China
| | - Xuemei Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zijun Yu
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Ziyi Yang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zhongqing Wang
- Department of Information Center, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Liang Sang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
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Deng J, Zhang W, Xu M, Zhou J. Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy. Abdom Radiol (NY) 2023; 48:3661-3676. [PMID: 37787962 DOI: 10.1007/s00261-023-04046-1] [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: 06/13/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 10/04/2023]
Abstract
Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.
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Affiliation(s)
- Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China.
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China.
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China.
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Ma S, Lu H, Jing G, Li Z, Zhang Q, Ma X, Chen F, Shao C, Lu Y, Wang H, Shen F. Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study. Front Med (Lausanne) 2023; 10:1276672. [PMID: 38105891 PMCID: PMC10722265 DOI: 10.3389/fmed.2023.1276672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Background Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Materials and methods Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. Results The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). Conclusion The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registration unique identifying number UIN Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.
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Affiliation(s)
- Shiyu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Zhihui Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fangying Chen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Colorectal Surgery, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
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13
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Tian C, Ma X, Lu H, Wang Q, Shao C, Yuan Y, Shen F. Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling. Front Med (Lausanne) 2023; 10:1326324. [PMID: 38105894 PMCID: PMC10722089 DOI: 10.3389/fmed.2023.1326324] [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: 10/23/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023] Open
Abstract
Background The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models. Methods A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, three types of DL-models were constructed: Model 1 trained on the total training dataset, Model 2 trained on the rectal-filling dataset, and Model 3 trained on the non-filling dataset. The diagnostic values were evaluated and compared using receiver operating characteristic (ROC) curve analysis, confusion matrix, net reclassification index (NRI), and decision curve analysis (DCA). Results The automatic segmentation showed excellent performance. The rectal-filling dataset exhibited superior results in terms of DSC and ASD (p = 0.006 and 0.017). The DL-models demonstrated significantly superior classification performance to the subjective evaluation in predicting T-stages for all test datasets (all p < 0.05). Among the models, Model 1 showcased the highest overall performance, with an area under the curve (AUC) of 0.958 and an accuracy of 0.962 in the filling test dataset. Conclusion This study highlighted the utility of DL-based automatic segmentation and classification models for preoperative T-stage assessment of RC on T2WI, particularly in the rectal-filling dataset. Compared with subjective evaluation, the models exhibited superior performance, suggesting their noticeable potential for enhancing clinical diagnosis and treatment practices.
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Affiliation(s)
- Chang Tian
- School of Information Science and Technology and School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Qian Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
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14
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Zhao Y, Li L, Han K, Li T, Duan J, Sun Q, Zhu C, Liang D, Chai N, Li ZC. A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer. Abdom Radiol (NY) 2023; 48:3332-3342. [PMID: 37716926 DOI: 10.1007/s00261-023-04037-2] [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: 05/11/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs). METHODS A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set. RESULTS The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis. CONCLUSION Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.
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Affiliation(s)
- Yuanshen Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China
| | - Ke Han
- Department of Gastroenterology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China
| | - Tao Li
- Department of Radiology, The First Medical Center of Chinese, PLA General Hospital, Beijing, China
| | - Jingxian Duan
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chaofan Zhu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Dong Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- National Innovation Center for Advanced Medical Devices, Shenzhen, China
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China.
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- National Innovation Center for Advanced Medical Devices, Shenzhen, China.
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Qu W, Yang J, Li J, Yuan G, Li S, Chu Q, Xie Q, Zhang Q, Cheng B, Li Z. Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy. Br J Radiol 2023; 96:20221112. [PMID: 37195026 PMCID: PMC10607397 DOI: 10.1259/bjr.20221112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 05/11/2023] [Indexed: 05/18/2023] Open
Abstract
OBJECTIVE This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA). METHODS 498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients. RESULTS The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution. CONCLUSION The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA. ADVANCES IN KNOWLEDGE This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.
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Affiliation(s)
- Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiannan Yang
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qingguo Xie
- Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | | | - Bin Cheng
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Yang J, Cao Y, Zhou F, Li C, Lv J, Li P. Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma. Front Oncol 2023; 13:1231497. [PMID: 37909025 PMCID: PMC10613647 DOI: 10.3389/fonc.2023.1231497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Background Differences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification. Methods Clinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model. Results We finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865-0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762-0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model. Conclusion MRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.
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Affiliation(s)
| | | | | | | | | | - Pu Li
- Clinical School of Obstetrics and Gynecology Center, Tianjin Medical University, Tianjin, China
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Gao Z, Yu Z, Zhang X, Chen C, Pan Z, Chen X, Lin W, Chen J, Zhuge Q, Shen X. Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images. Front Oncol 2023; 13:1265366. [PMID: 37869090 PMCID: PMC10587601 DOI: 10.3389/fonc.2023.1265366] [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: 07/22/2023] [Accepted: 09/15/2023] [Indexed: 10/24/2023] Open
Abstract
Background Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.
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Affiliation(s)
- Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Xiang Zhang
- Wenzhou Data Management and Development Group Co., Ltd., Wenzhou, Zhejiang, China
| | - Chun Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaodong Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weihong Lin
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jun Chen
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qichuan Zhuge
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xian Shen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Assumpção PPD, Silva JMCD, Calcagno DQ, Barra WF, Ishak G, Kassab P. OLIGOMETASTASIS IN GASTRIC CANCER TREATMENT: IS THERE A PLACE FOR THE SURGEON? ARQUIVOS BRASILEIROS DE CIRURGIA DIGESTIVA : ABCD = BRAZILIAN ARCHIVES OF DIGESTIVE SURGERY 2023; 36:e1752. [PMID: 37729281 PMCID: PMC10510098 DOI: 10.1590/0102-672020230034e1752] [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: 04/04/2023] [Accepted: 06/01/2023] [Indexed: 09/22/2023]
Abstract
Metastatic gastric cancer traditionally hinders surgical treatment options, confining them to palliative procedures. The presence of metastases in these tumors is classified as M1, irrespective of their characteristics, quantity, or location. However, oligometastatic disease emerged as an intermediate state between localized and widely disseminated cancer. It exhibits diverse patterns based on metastatic disease extent, type, and location. Adequately addressing this distinctive metastatic state necessitates tailored strategies that surpass the realm of palliative care. Differentprimary tumor types present discernible scenarios of oligometastatic disease, including preferred sites of occurrence and chronological progression. Due to the novelty of this theme and the heterogeneity of the disease, uncertainties still exist, and the ability to provide confident guidelines is challenging. Currently, there are no effective predictors to determine the response and provide clear indications for surgical interventions and systemic treatments in oligometastatic disease. Treatment decisions are commonly based on apparent disease control by systemic therapies, with a short observation period and imaging assessments. Nonetheless, the inherent risk of misinterpretation remains a constant concern. The emergence of novel technologies and therapeutic modalities, such as immunotherapy, cellular therapy, and adoptive therapies, holds the potential to reshape the landscape of surgical treatment for the oligometastatic disease in gastric cancer, expanding the surgeon's role in this multidisciplinary approach. Prospective tools for patient selection in oligometastatic gastric cancer are being explored. Using non-invasive, cost-effective, widely available imaging techniques that provide real-time information may revolutionize medical practice, ensuring precision medicine accessibility, even in resource-constrained small healthcare facilities. Incorporating molecular classifications, liquid biopsies, and radiomic analysis in a complementary protocol will augment patient selection precision for surgical intervention in oligometastasis. Hopefully, these advancements will render surgeries unnecessary in many cases by providing highly effective alternative treatments.
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Affiliation(s)
- Paulo Pimentel de Assumpção
- Universidade Federal do Pará, Oncology Research Center - Belém (PA), Brazil
- Universidade Federal do Pará, João de Barros Barreto University Hospital, General Surgery and Digestive Tract Service - Belém (PA), Brazil
| | | | | | | | - Geraldo Ishak
- Universidade Federal do Pará, Oncology Research Center - Belém (PA), Brazil
- Universidade Federal do Pará, João de Barros Barreto University Hospital, General Surgery and Digestive Tract Service - Belém (PA), Brazil
| | - Paulo Kassab
- Faculdade de Ciências Médicas da Santa Casa de São Paulo, Department of Surgery - São Paulo (SP), Brazil
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Jiang T, Zhao Z, Liu X, Shen C, Mu M, Cai Z, Zhang B. Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study. Front Oncol 2023; 13:1161237. [PMID: 37731636 PMCID: PMC10507631 DOI: 10.3389/fonc.2023.1161237] [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: 02/08/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
Background Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. Methods The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. Results Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. Conclusion The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. Study registration This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).
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Affiliation(s)
- Tianxiang Jiang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhou Zhao
- Department of Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Xueting Liu
- Department of Medical Discipline Construction, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyong Shen
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Mingchun Mu
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaolun Cai
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Zhang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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21
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Chen PT, Shih TTF. Editorial for "Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer". J Magn Reson Imaging 2023; 58:924-925. [PMID: 36762872 DOI: 10.1002/jmri.28637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Affiliation(s)
- Po-Ting Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Tiffany Ting Fang Shih
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan
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Zhang QW, Yang PP, Gao YJY, Li ZH, Yuan Y, Li SJ, Duan SF, Shao CW, Hao Q, Lu Y, Chen Q, Shen F. Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study. Cancer Imaging 2023; 23:71. [PMID: 37488597 PMCID: PMC10367237 DOI: 10.1186/s40644-023-00584-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/09/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases. METHODS Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model. RESULTS The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts. CONCLUSIONS This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.
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Affiliation(s)
- Qian-Wen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Pan-Pan Yang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong-Jun-Yi Gao
- Department of Emergency, the Eighth Medical Center of Chinese, PLA General Hospital, 17 Heishanhu Rd, Haidian District, Beijing, 100091, China
| | - Zhi-Hui Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Si-Jie Li
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Shao-Feng Duan
- GE Healthcare China, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China
| | - Cheng-Wei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Qiang Hao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Chen
- Department of Health Statistics, The Navy Medical University, Shanghai, 200433, China.
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China.
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Yang ZH, Han YJ, Cheng M, Wang R, Li J, Zhao HP, Gao JB. Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm. Front Oncol 2023; 13:1143291. [PMID: 37409252 PMCID: PMC10319063 DOI: 10.3389/fonc.2023.1143291] [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: 01/13/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
Purpose The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN). Methods and Materials A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated. Results The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice. Conclusions The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.
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Affiliation(s)
- Zhi-hao Yang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi-jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Li
- Department of Radiology, Affiliated Tumor Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-ping Zhao
- Department of Radiology, Shanxi Provincial People’s Hospital, Xi’an, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Qu W, Zhou Z, Yuan G, Li S, Li J, Chu Q, Zhang Q, Xie Q, Li Z, Kamel IR. Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis? Eur J Radiol 2023; 164:110857. [PMID: 37172441 DOI: 10.1016/j.ejrad.2023.110857] [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: 11/02/2022] [Revised: 03/22/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023]
Abstract
PURPOSE To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed. METHODS A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort. RESULTS In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUCLASSO = 0.743, 95% CI: 0.590-0.896; AUCPCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUConlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUCPCACli = 0.880, 95% CI: 0.776-0.983; AUCLASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUCFNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%. CONCLUSIONS The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.
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Affiliation(s)
- Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Ziling Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qingpeng Zhang
- Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region; The Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Qingguo Xie
- Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Ihab R Kamel
- Johns Hopkins Hospital, Russell H Morgan Department of Radiology & Radiological Science, 600 N Wolfe St, Baltimore, MD 21205, USA.
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Giandola T, Maino C, Marrapodi G, Ratti M, Ragusi M, Bigiogera V, Talei Franzesi C, Corso R, Ippolito D. Imaging in Gastric Cancer: Current Practice and Future Perspectives. Diagnostics (Basel) 2023; 13:diagnostics13071276. [PMID: 37046494 PMCID: PMC10093088 DOI: 10.3390/diagnostics13071276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/19/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.
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Kang C, Sun P, Yang R, Zhang C, Ning W, Liu H. CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study. Front Oncol 2023; 13:1094768. [PMID: 37064100 PMCID: PMC10103838 DOI: 10.3389/fonc.2023.1094768] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
PurposeThis study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC).MethodsThis retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan–Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness.ResultsOverall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model.ConclusionFor patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.
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Affiliation(s)
- Chunmiao Kang
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Pengfeng Sun
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Runqin Yang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Changming Zhang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Wenfeng Ning
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hongsheng Liu
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
- *Correspondence: Hongsheng Liu,
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [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: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study. J Clin Med 2023; 12:jcm12062280. [PMID: 36983281 PMCID: PMC10059677 DOI: 10.3390/jcm12062280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Objects: To evaluate the prognostic value of radiomics features extracted from 18F-FDG-PET/CT images and integrated with clinical characteristics and conventional PET/CT metrics in newly diagnosed multiple myeloma (NDMM) patients. Methods: We retrospectively reviewed baseline clinical information and 18F-FDG-PET/CT imaging data of MM patients with 18F-FDG-PET/CT. Multivariate Cox regression models involving different combinations were constructed, and stepwise regression was performed: (1) radiomics features of PET/CT alone (Rad Model); (2) Using clinical data (including clinical/laboratory parameters and conventional PET/CT metrics) only (Cli Model); (3) Combination radiomics features and clinical data (Cli-Rad Model). Model performance was evaluated by C-index and Net Reclassification Index (NRI). Results: Ninety-eight patients with NDMM who underwent 18F-FDG-PET/CT between 2014 and 2019 were included in this study. Combining radiomics features from PET/CT with clinical data showed higher prognostic performance than models with radiomics features or clinical data alone (C-index 0.790 vs. 0.675 vs. 0.736 in training cohort; 0.698 vs. 0.651 vs. 0.563 in validation cohort; AUC 0.761, sensitivity 56.7%, specificity 85.7%, p < 0.05 in training cohort and AUC 0.650, sensitivity 80.0%, specificity78.6%, p < 0.05 in validation cohort) When clinical data was combined with radiomics, an increase in the performance of the model was observed (NRI > 0). Conclusions: Radiomics features extracted from the PET and CT components of baseline 18F-FDG-PET/CT images may become an effective complement to provide prognostic information; therefore, radiomics features combined with clinical characteristic may provide clinical value for MM prognosis prediction.
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Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. BIOLOGY 2023; 12:biology12020213. [PMID: 36829492 PMCID: PMC9952965 DOI: 10.3390/biology12020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Salerno, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Orlando Catalano
- Radiology Unit, Istituto Diagnostico Varelli, Via Cornelia dei Gracchi 65, 80126 Naples, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56216 Pisa, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Federica Flammia
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federico Bruno
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Alessandro Ottaiano
- SSD Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori IRCCS-Fondazione G. Pascale, 80130 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15020351. [PMID: 36672301 PMCID: PMC9857317 DOI: 10.3390/cancers15020351] [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: 11/16/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
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Tan X, Yang X, Hu S, Ge Y, Wu Q, Wang J, Sun Z. Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:49-61. [PMID: 36314190 DOI: 10.3233/xst-221291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
PURPOSE To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment. MATERIALS AND METHODS The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model. RESULTS Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model. CONCLUSION CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.
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Affiliation(s)
- Xiaoying Tan
- Jiangnan University, Wuxi City, Jiangsu Province, China
| | - Xiao Yang
- Jiangnan University, Wuxi City, Jiangsu Province, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China
| | - Qiong Wu
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Jun Wang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zongqiong Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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