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Zhang B, Huang S, Zhou C, Zhu J, Chen T, Feng S, Huang C, Wang Z, Wu S, Liu C, Zhan X. Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods. Comput Assist Surg (Abingdon) 2024; 29:2345066. [PMID: 38860617 DOI: 10.1080/24699322.2024.2345066] [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: 06/12/2024] Open
Abstract
BACKGROUND Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.
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Affiliation(s)
- Bin Zhang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Orthopaedics, The Guizhou Hospital of Beijing Jishuitan Hospital, Guiyang, China
| | - Shengsheng Huang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chenxing Zhou
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jichong Zhu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Tianyou Chen
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Sitan Feng
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengqian Huang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zequn Wang
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shaofeng Wu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chong Liu
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xinli Zhan
- Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Chen X, Chen Q, Liu Y, Qiu Y, Lv L, Zhang Z, Yin X, Shu F. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT. CANCER INNOVATION 2024; 3:e135. [PMID: 38948899 PMCID: PMC11212276 DOI: 10.1002/cai2.135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 07/02/2024]
Abstract
Background Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma. Methods We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots. Results The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95-0.99) on the training set and 0.90 (95% CI: 0.82-0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89-0.96) on the training set and 0.91 (95% CI: 0.85-0.97) on the validation set. Conclusions MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.
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Affiliation(s)
- Xiong Chen
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
| | - Qinchang Chen
- Department of Pediatric Cardiology, Guangdong Provincial People's HospitalGuangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Structural Heart DiseaseGuangzhouChina
| | - Yuanfang Liu
- Department of Radiology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Ya Qiu
- Department of Radiologythe First People's Hospital of Kashi PrefectureKashiChina
| | - Lin Lv
- Medical SchoolSun Yat‐sen UniversityGuangzhouChina
| | - Zhengtao Zhang
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
| | - Xuntao Yin
- Department of RadiologyGuangzhou Women and Children's Medical CenterGuangzhouChina
| | - Fangpeng Shu
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
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Zhang X, Wu S, Zu X, Li X, Zhang Q, Ren Y, Qian X, Tong S, Li H. Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer. Front Oncol 2024; 14:1438923. [PMID: 39359429 PMCID: PMC11445231 DOI: 10.3389/fonc.2024.1438923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 08/29/2024] [Indexed: 10/04/2024] Open
Abstract
Purpose Accurate preoperative identification of Human epidermal growth factor receptor 2 (HER2) low expression breast cancer (BC) is critical for clinical decision-making. Our aim was to use machine learning methods to develop and validate an ultrasound-based radiomics nomogram for predicting HER2-low expression in BC. Methods In this retrospective study, 222 patients (108 HER2-0 expression and 114 HER2-low expression) with BC were included. The enrolled patients were randomly divided into a training cohort and a test cohort with a ratio of 8:2. The tumor region of interest was manually delineated from ultrasound image, and radiomics features were subsequently extracted. The features underwent dimension reduction using the least absolute shrinkage and selection operator (LASSO) algorithm, and rad-score were calculated. Five machine learning algorithms were applied for training, and the algorithm demonstrating the best performance was selected to construct a radiomics (USR) model. Clinical risk factors were integrated with rad-score to construct the prediction model, and a nomogram was plotted. The performance of the nomogram was assessed using receiver operating characteristic curve and decision curve analysis. Results A total of 480 radiomics features were extracted, out of which 11 were screened out. The majority of the extracted features were wavelet features. Subsequently, the USR model was established, and rad-scores were computed. The nomogram, incorporating rad-score, tumor shape, border, and microcalcification, achieved the best performance in both the training cohort (AUC 0.89; 95%CI 0.836-0.936) and the test cohort (AUC 0.84; 95%CI 0.722-0.958), outperforming both the USR model and clinical model. The calibration curves showed satisfactory consistency, and DCA confirmed the clinical utility of the nomogram. Conclusion The nomogram model based on ultrasound radiomics exhibited high prediction value for HER2-low BC.
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Affiliation(s)
- Xueling Zhang
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Shaoyou Wu
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiao Zu
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiaojing Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qing Zhang
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Yongzhen Ren
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Xiaoqin Qian
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Shan Tong
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Hongbo Li
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Ultrasound Medicine, People’s Hospital of Longhua, Shenzhen, China
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Li C, Lu X, Zhang F, Huang S, Ding L, Wang H, Chen S. Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis. BMC Cancer 2024; 24:1151. [PMID: 39289658 PMCID: PMC11406734 DOI: 10.1186/s12885-024-12912-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 09/06/2024] [Indexed: 09/19/2024] Open
Abstract
OBJECTIVE We explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB). METHODS This retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity. RESULTS There were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348-41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS. CONCLUSION ASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.
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Affiliation(s)
- Chao Li
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Xueyuan Lu
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Fengxian Zhang
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Road, Shanghai, 200433, China
| | - Shuo Huang
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Lin Ding
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Hui Wang
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China.
| | - Suyun Chen
- Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China.
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Singh SB, Sarrami AH, Gatidis S, Varniab ZS, Chaudhari A, Daldrup-Link HE. Applications of Artificial Intelligence for Pediatric Cancer Imaging. AJR Am J Roentgenol 2024; 223:e2431076. [PMID: 38809123 DOI: 10.2214/ajr.24.31076] [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: 05/30/2024]
Abstract
Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.
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Affiliation(s)
- Shashi B Singh
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Amir H Sarrami
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Sergios Gatidis
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Zahra S Varniab
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Akshay Chaudhari
- Department of Radiology, Integrative Biomedical Imaging Informatics (IBIIS), Stanford University School of Medicine, Stanford University, Stanford, CA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford University, Stanford, CA
| | - Heike E Daldrup-Link
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
- Department of Pediatrics, Pediatric Hematology-Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA
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Koska IO, Ozcan HN, Tan AA, Beydogan B, Ozer G, Oguz B, Haliloglu M. Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children. Eur Radiol 2024; 34:5016-5027. [PMID: 38311701 PMCID: PMC11255001 DOI: 10.1007/s00330-024-10589-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 02/06/2024]
Abstract
OBJECTIVES Machine learning methods can be applied successfully to various medical imaging tasks. Our aim with this study was to build a robust classifier using radiomics and clinical data for preoperative diagnosis of Wilms tumor (WT) or neuroblastoma (NB) in pediatric abdominal CT. MATERIAL AND METHODS This is a single-center retrospective study approved by the Institutional Ethical Board. CT scans of consecutive patients diagnosed with WT or NB admitted to our hospital from January 2005 to December 2021 were evaluated. Three distinct datasets based on clinical centers and CT machines were curated. Robust, non-redundant, high variance, and relevant radiomics features were selected using data science methods. Clinically relevant variables were integrated into the final model. Dice score for similarity of tumor ROI, Cohen's kappa for interobserver agreement among observers, and AUC for model selection were used. RESULTS A total of 147 patients, including 90 WT (mean age 34.78 SD: 22.06 months; 43 male) and 57 NB (mean age 23.77 SD:22.56 months; 31 male), were analyzed. After binarization at 24 months cut-off, there was no statistically significant difference between the two groups for age (p = .07) and gender (p = .54). CT clinic radiomics combined model achieved an F1 score of 0.94, 0.93 accuracy, and an AUC 0.96. CONCLUSION In conclusion, the CT-based clinic-radiologic-radiomics combined model could noninvasively predict WT or NB preoperatively. Notably, that model correctly predicted two patients, which none of the radiologists could correctly predict. This model may serve as a noninvasive preoperative predictor of NB/WT differentiation in CT, which should be further validated in large prospective models. CLINICAL RELEVANCE STATEMENT CT-based clinic-radiologic-radiomics combined model could noninvasively predict Wilms tumor or neuroblastoma preoperatively. KEY POINTS • CT radiomics features can predict Wilms tumor or neuroblastoma from abdominal CT preoperatively. • Integrating clinic variables may further improve the performance of the model. • The performance of the combined model is equal to or greater than human readers, depending on the lesion size.
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Affiliation(s)
- Ilker Ozgur Koska
- Department of Radiology, Behcet Uz Children's Hospital, Konak İzmir, Turkey.
| | - H Nursun Ozcan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Aziz Anil Tan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
- Department of Radiology, Sincan Training and Research Hospital, Ankara, Turkey
| | - Beyza Beydogan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Gozde Ozer
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Berna Oguz
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Mithat Haliloglu
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
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Si J, Wang H, Xie M, Yang Y, Li J, Wang F, Chen X, He L. The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas. Quant Imaging Med Surg 2024; 14:5630-5641. [PMID: 39143994 PMCID: PMC11320520 DOI: 10.21037/qims-24-122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/12/2024] [Indexed: 08/16/2024]
Abstract
Background Lymphoma is a common malignant tumor in children. The pathologic subtyping of lymphoma is high complex, and the treatment options vary. The different pathologic subtypes of lymphomas have no significant differences on computed tomography (CT) images. As it is a hematologic disease, patients with lymphoma often show abnormalities in the spleen, and so the aim of this study was to construct a model for differentiating Burkitt lymphoma (BL) from lymphoblastic lymphoma through the extraction of radiomic features of the spleen from CT images. This could provide an efficient, noninvasive method that can differentiate the common pathological subtypes in patients with pediatric lymphoma. Methods The clinical data and imaging data of 48 patients with lymphoblastic lymphoma and 61 patients with BL were retrospectively analyzed. The dataset was divided into a training set (n=76) and a test set (n=33) through complete randomization. Radiomics features of the spleen were separately extracted from CT images in the noncontrast enhanced, arterial, and venous phases. These phase-specific features were integrated to construct fusion models. Three classifiers, quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM), were employed to build the models. Results The fusion model exhibited superior performance compared to individual models. There was no significant difference between the fusion models constructed by QDA and LR in either the training set or the test set. Among the four fusion models constructed with the SVM classifier, SVM_4 emerged as the best performing model. The area under the curve, sensitivity, specificity, and F1-score of the SVM_4 model were 0.967 [95% confidence interval (CI): 0.935-0.998], 0.86, 0.97, and 0.913 in the training set, respectively, and 0.754 (95% CI: 0.584-0.924), 0.611, 0.867, and 0.71 in the test set, respectively. Conclusions The radiomics features of the spleen demonstrated the capability to distinguish between the two most common lymphoma subtypes in pediatric patients. This noninvasive approach holds promise for efficient and accurate discrimination.
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Affiliation(s)
- Jiajun Si
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Haoru Wang
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Mingye Xie
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Yanlin Yang
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Jun Li
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xin Chen
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
| | - Ling He
- Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
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Safdar NM, Galaria A. From Nicki Minaj to Neuroblastoma: What Rigorous Approaches to Rhythms and Radiomics Have in Common. Radiol Artif Intell 2024; 6:e240350. [PMID: 39017031 PMCID: PMC11294945 DOI: 10.1148/ryai.240350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/18/2024]
Affiliation(s)
- Nabile M. Safdar
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University, 1364 Clifton Rd NE, Ste D112, Atlanta, GA 30322 (N.M.S.); and Johns Hopkins University, Baltimore, Md (A.G.)
| | - Alina Galaria
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University, 1364 Clifton Rd NE, Ste D112, Atlanta, GA 30322 (N.M.S.); and Johns Hopkins University, Baltimore, Md (A.G.)
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Veiga-Canuto D, Fernández-Patón M, Cerdà Alberich L, Jiménez Pastor A, Gomis Maya A, Carot Sierra JM, Sangüesa Nebot C, Martínez de las Heras B, Pötschger U, Taschner-Mandl S, Neri E, Cañete A, Ladenstein R, Hero B, Alberich-Bayarri Á, Martí-Bonmatí L. Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors. Radiol Artif Intell 2024; 6:e230208. [PMID: 38864742 PMCID: PMC11294951 DOI: 10.1148/ryai.230208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 04/22/2024] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Keywords: Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.
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Affiliation(s)
- Diana Veiga-Canuto
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Matías Fernández-Patón
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Leonor Cerdà Alberich
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ana Jiménez Pastor
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Armando Gomis Maya
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Jose Miguel Carot Sierra
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Cinta Sangüesa Nebot
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Blanca Martínez de las Heras
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ulrike Pötschger
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Sabine Taschner-Mandl
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Emanuele Neri
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Adela Cañete
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ruth Ladenstein
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Barbara Hero
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ángel Alberich-Bayarri
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Luis Martí-Bonmatí
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
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Karam S, Gebreil A, Alksas A, Balaha HM, Khalil A, Ghazal M, Contractor S, El-Baz A. Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review. Biomedicines 2024; 12:1455. [PMID: 39062028 PMCID: PMC11274555 DOI: 10.3390/biomedicines12071455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/17/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Wilms tumor (WT), or nephroblastoma, is the predominant renal malignancy in the pediatric population. This narrative review explores the evolution of personalized care strategies for WT, synthesizing critical developments in molecular diagnostics and treatment approaches to enhance patient-specific outcomes. We surveyed recent literature from the last five years, focusing on high-impact research across major databases such as PubMed, Scopus, and Web of Science. Diagnostic advancements, including liquid biopsies and diffusion-weighted MRI, have improved early detection precision. The prognostic significance of genetic markers, particularly WT1 mutations and miRNA profiles, is discussed. Novel predictive tools integrating genetic and clinical data to anticipate disease trajectory and therapy response are explored. Progressive treatment strategies, particularly immunotherapy and targeted agents such as HIF-2α inhibitors and GD2-targeted immunotherapy, are highlighted for their role in personalized treatment protocols, especially for refractory or recurrent WT. This review underscores the necessity for personalized management supported by genetic insights, with improved survival rates for localized disease exceeding 90%. However, knowledge gaps persist in therapies for high-risk patients and strategies to reduce long-term treatment-related morbidity. In conclusion, this narrative review highlights the need for ongoing research, particularly on the long-term outcomes of emerging therapies and integrating multi-omic data to inform clinical decision-making, paving the way for more individualized treatment pathways.
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Affiliation(s)
- Salma Karam
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (S.K.); (A.G.); (A.A.); (H.M.B.)
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (S.K.); (A.G.); (A.A.); (H.M.B.)
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (S.K.); (A.G.); (A.A.); (H.M.B.)
| | - Hossam Magdy Balaha
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (S.K.); (A.G.); (A.A.); (H.M.B.)
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates;
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (S.K.); (A.G.); (A.A.); (H.M.B.)
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Chen X, Wang H, Xia Y, Shi F, He L, Liu E. The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma. Discov Oncol 2024; 15:201. [PMID: 38822860 PMCID: PMC11144178 DOI: 10.1007/s12672-024-01067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/28/2024] [Indexed: 06/03/2024] Open
Abstract
OBJECTIVE Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China.
| | - Enmei Liu
- Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, 400014, China.
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Wang H, Yu C, Ding H, Zhang L, Chen X, He L. Computed Tomography-Based Radiomics Signature for Predicting Segmental Chromosomal Aberrations at 1p36 and 11q23 in Pediatric Neuroblastoma. J Comput Assist Tomogr 2024; 48:472-479. [PMID: 38013242 DOI: 10.1097/rct.0000000000001564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVE This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma. METHODS Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI). RESULTS Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated. CONCLUSIONS Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.
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Affiliation(s)
- Haoru Wang
- From the Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Kim J, Choi YH, Yoon H, Lim HJ, Han JW, Lee MJ. Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study. Yonsei Med J 2024; 65:293-301. [PMID: 38653568 PMCID: PMC11045346 DOI: 10.3349/ymj.2023.0192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE This study aimed to predict high-risk neuroblastoma among neuroblastic tumors using radiomics features extracted from MRI. MATERIALS AND METHODS Pediatric patients (age≤18 years) diagnosed with neuroblastic tumors who had pre-treatment MR images available were enrolled from institution A from January 2010 to November 2019 (training set) and institution B from January 2016 to January 2022 (test set). Segmentation was performed with regions of interest manually drawn along tumor margins on the slice with the widest tumor area by two radiologists. First-order and texture features were extracted and intraclass correlation coefficients (ICCs) were calculated. Multivariate logistic regression (MLR) and random forest (RF) models from 10-fold cross-validation were built using these features. The trained MLR and RF models were tested in an external test set. RESULTS Thirty-two patients (M:F=23:9, 26.0±26.7 months) were in the training set and 14 patients (M:F=10:4, 33.4±20.4 months) were in the test set with radiomics features (n=930) being extracted. For 10 of the most relevant features selected, intra- and inter-observer variability was moderate to excellent (ICCs 0.633-0.911, 0.695-0.985, respectively). The area under the receiver operating characteristic curve (AUC) was 0.94 (sensitivity 67%, specificity 91%, and accuracy 84%) for the MLR model and the average AUC was 0.83 (sensitivity 44%, specificity 87%, and accuracy 75%) for the RF model from 10-fold cross-validation. In the test set, AUCs of the MLR and RF models were 0.94 and 0.91, respectively. CONCLUSION An MRI-based radiomics model can help predict high-risk neuroblastoma among neuroblastic tumors.
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Affiliation(s)
- Jisoo Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Haesung Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Ji Lim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jung Woo Han
- Department of Pediatric Hematology-Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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Qian LD, Zhou ZA, Li SQ, Liu J, Zhang SX, Ren JL, Wang W, Yang J. 18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category. Quant Imaging Med Surg 2024; 14:3131-3145. [PMID: 38617169 PMCID: PMC11007507 DOI: 10.21037/qims-23-494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 02/10/2024] [Indexed: 04/16/2024]
Abstract
Background The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.
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Affiliation(s)
- Luo-Dan Qian
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zi-Ang Zhou
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Si-Qi Li
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jun Liu
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shu-Xin Zhang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Wei Wang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jigang Yang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Xie LL, Gong Y, Dong KR, Shen C, Duan B, Dong R. Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images. World J Oncol 2024; 15:81-89. [PMID: 38274719 PMCID: PMC10807921 DOI: 10.14740/wjon1744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
Background The aim of the study was to employ a combination of radiomic indicators based on computed tomography (CT) imaging and machine learning (ML), along with deep learning (DL), to differentiate between adrenal hematoma and adrenal neuroblastoma in neonates. Methods A total of 76 neonates were included in this retrospective study (40 with neuroblastomas and 36 with adrenal hematomas) who underwent CT and divided into a training group (n = 38) and a testing group (n = 38). The regions of interest (ROIs) were segmented by two radiologists to extract radiomics features using Pyradiomics package. ML classifications were done using support vector machine (SVM), AdaBoost, Extra Trees, gradient boosting, multi-layer perceptron (MLP), and random forest (RF). EfficientNets was employed and classified, based on radiometrics. The area under curve (AUC) of the receiver operating characteristic (ROC) was calculated to assess the performance of each model. Results Among all features, the least absolute shrinkage and selection operator (LASSO) logistic regression selected nine features. These radiomics features were used to construct radiomics model. In the training cohort, the AUCs of SVM, MLP and Extra Trees models were 0.967, 0.969 and 1.000, respectively. The corresponding AUCs of the test cohort were 0.985, 0.971 and 0.958, respectively. In the classification task, the AUC of the DL framework was 0.987. Conclusion ML decision classifiers and DL framework constructed from CT-based radiomics features offered a non-invasive method to differentiate neonatal adrenal hematoma from neuroblastoma and performed better than the clinical experts.
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Affiliation(s)
- Lu Lu Xie
- Shanghai Institute of Infectious Disease and Biosecurity, Children’s Hospital of Fudan University, Shanghai 201102, China
- Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, and Key Laboratory of Neonatal Disease, Ministry of Health, Children’s Hospital of Fudan University, Shanghai 201102, China
| | - Ying Gong
- Department of Radiology, Children’s Hospital of Fudan University, Shanghai 201102, China
| | - Kui Ran Dong
- Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, and Key Laboratory of Neonatal Disease, Ministry of Health, Children’s Hospital of Fudan University, Shanghai 201102, China
| | - Chun Shen
- Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, and Key Laboratory of Neonatal Disease, Ministry of Health, Children’s Hospital of Fudan University, Shanghai 201102, China
| | - Bo Duan
- Department of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan University, Shanghai 201102, China
| | - Rui Dong
- Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, and Key Laboratory of Neonatal Disease, Ministry of Health, Children’s Hospital of Fudan University, Shanghai 201102, China
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Jahangiri L. Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review. Med Sci (Basel) 2024; 12:5. [PMID: 38249081 PMCID: PMC10801560 DOI: 10.3390/medsci12010005] [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: 11/04/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.
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Affiliation(s)
- Leila Jahangiri
- School of Science and Technology, Nottingham Trent University, Clifton Site, Nottingham NG11 8NS, UK;
- Division of Cellular and Molecular Pathology, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
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Deng Y, Wang H, He L. CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children. BMC Med Imaging 2024; 24:13. [PMID: 38182986 PMCID: PMC10768092 DOI: 10.1186/s12880-023-01184-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients. METHODS We retrospectively enrolled 83 cases of WT and 33 cases of CCSK. These cases were randomly stratified into a training set (n = 81) and a test set (n = 35). Several imaging features from the nephrographic phase were analyzed, including the maximum tumor diameter, the ratio of the maximum CT value of the tumor solid portion to the mean CT value of the contralateral renal vein (CTmax/CT renal vein), and the presence of dilated peritumoral cysts. Radiomics features from corticomedullary phase were extracted, selected, and subsequently integrated into a logistic regression model. We evaluated the model's performance using the area under the curve (AUC), 95% confidence interval (CI), and accuracy. RESULTS In the training set, there were statistically significant differences in the maximum tumor diameter (P = 0.021) and the presence of dilated peritumoral cysts (P = 0.005) between WT and CCSK, whereas in the test set, no statistically significant differences were observed (P > 0.05). The radiomics model, constructed using four radiomics features, demonstrated strong performance in the training set with an AUC of 0.889 (95% CI: 0.811-0.967) and an accuracy of 0.864. Upon evaluation using fivefold cross-validation in the training set, the AUC remained high at 0.863 (95% CI: 0.774-0.952), with an accuracy of 0.852. In the test set, the radiomics model achieved an AUC of 0.792 (95% CI: 0.616-0.968) and an accuracy of 0.857. CONCLUSION CT radiomics proves to be diagnostically valuable for distinguishing between WT and CCSK in pediatric cases.
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Affiliation(s)
- Yaxin Deng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China.
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Wang H, Chen X, He L. A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges. Pediatr Radiol 2023; 53:2742-2755. [PMID: 37945937 DOI: 10.1007/s00247-023-05792-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various imaging modalities, including computed tomography, magnetic resonance imaging, and positron emission tomography, are utilized to assess neuroblastoma. Nevertheless, these conventional imaging modalities have limitations in providing quantitative information for accurate diagnosis and prognosis. Radiomics, an emerging technique, can extract intricate medical imaging information that is imperceptible to the human eye and transform it into quantitative data. In conjunction with deep learning algorithms, radiomics holds great promise in complementing existing imaging modalities. The aim of this review is to showcase the potential of radiomics and deep learning advancements to enhance the diagnostic capabilities of current imaging modalities for neuroblastoma.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
- Chongqing Key Laboratory of Pediatrics, Chongqing, China.
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Qian LD, Zhang SX, Li SQ, Feng LJ, Zhou ZA, Liu J, Zhang MY, Yang JG. Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature. Insights Imaging 2023; 14:205. [PMID: 38001240 PMCID: PMC10673749 DOI: 10.1186/s13244-023-01493-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 07/31/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVES To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients. METHODS A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts. RESULTS The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757-0.963) and 0.824 (95% CI, 0.657-0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility. CONCLUSION Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy. CRITICAL RELEVANCE STATEMENT Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. KEY POINTS • Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification.
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Affiliation(s)
- Luo-Dan Qian
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Shu-Xin Zhang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Si-Qi Li
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Li-Juan Feng
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zi-Ang Zhou
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Jun Liu
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Ming-Yu Zhang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
| | - Ji-Gang Yang
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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Wang X, Wang X, Wu T, Hu L, Xu M, Tang J, Li X, Zhong Y. Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors. Medicine (Baltimore) 2023; 102:e35690. [PMID: 38013377 PMCID: PMC10681616 DOI: 10.1097/md.0000000000035690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/27/2023] [Indexed: 11/29/2023] Open
Abstract
This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy and select features. A risk model was built to stratify low-, intermediate-, and high-risk groups. An image-defined risk factor (IDRFs) model was developed to classify 266 patients with malignant PNTs and one or more IDRFs into high-risk and non-high-risk groups. The performance of the predictive models was evaluated with respect to accuracy (Acc) and receiver operating characteristic (ROC) curve, including the area under the ROC curve (AUC). The risk model demonstrated good discrimination capability, with an area under the curve (AUC) of 0.903 to distinguish high-risk from non-high-risk groups, and 0.747 to classify intermediate- and low-risk groups. In the IDRF-based risk model with the number of IDRFs, the AUC was 0.876 for classifying the high-risk and non-high-risk groups. Radiomic analysis based on preoperative CT images has the potential to stratify the risk of pediatric malignant PNTs. It had outstanding efficiency in distinguishing patients in the high-risk group, and this predictive model of risk stratification could assist in selecting optimal aggressive treatment options.
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Affiliation(s)
- Xiaoxia Wang
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Xinrong Wang
- General Electric China Co., Ltd, Shanghai, China
| | - Tingfan Wu
- General Electric China Co., Ltd, Shanghai, China
| | - Liwei Hu
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Min Xu
- Department of Surgery, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Jingyan Tang
- Department of Hematology and Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Xin Li
- General Electric China Co., Ltd, Shanghai, China
| | - Yumin Zhong
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
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Ghosh A, Yekeler E, Teixeira SR, Dalal D, States L. Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas. Eur Radiol 2023; 33:6726-6735. [PMID: 37178203 DOI: 10.1007/s00330-023-09628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/18/2023] [Accepted: 02/26/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas. METHODS A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set. RESULTS The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%. CONCLUSION The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models. KEY POINTS • MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Cincinnati Children's Hospital and Medical Centre, Cincinnati, OH, USA.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Ensar Yekeler
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sara Reis Teixeira
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Deepa Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa States
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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22
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Yeow LY, Teh YX, Lu X, Srinivasa AC, Tan E, Tan TSE, Tang PH, Kn BP. Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization. J Comput Assist Tomogr 2023; 47:786-795. [PMID: 37707410 DOI: 10.1097/rct.0000000000001480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
OBJECTIVE MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification. METHODS Data from pretreatment contrast-enhanced computed tomography scans and MYCN status from 47 cases of pediatric neuroblastomas treated at a tertiary children's hospital from 2009 to 2020 were reviewed. Automated tumor segmentation and grading pipeline includes (1) a modified U-Net for tumor segmentation; (2) extraction of radiomic textural features; (3) feature-based ComBat harmonization for removal of variabilities across scanners; (4) feature selection using 2 approaches, namely, ( a ) an ensemble approach and ( b ) stepwise forward-and-backward selection method using logistic regression classifier; and (5) radiomics features-based classification of MYCN gene amplification using machine learning classifiers. RESULTS Median train/test Dice score for modified U-Net was 0.728/0.680. The top 3 features from the ensemble approach were neighborhood gray-tone difference matrix (NGTDM) busyness, NGTDM strength, and gray-level run-length matrix (GLRLM) low gray-level run emphasis, whereas those from the stepwise approach were GLRLM low gray-level run emphasis, GLRLM high gray-level run emphasis, and NGTDM coarseness. The top-performing tumor classification algorithm achieved a weighted F1 score of 97%, an area under the receiver operating characteristic curve of 96.9%, an accuracy of 96.97%, and a negative predictive value of 100%. Harmonization-based tumor classification improved the accuracy by 2% to 3% for all classifiers. CONCLUSION The proposed end-to-end framework achieved high accuracy for MYCN gene amplification status classification.
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Affiliation(s)
- Ling Yun Yeow
- From the Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)
| | - Yu Xuan Teh
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University
| | | | | | - Eelin Tan
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Timothy Shao Ern Tan
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Phua Hwee Tang
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Bhanu Prakash Kn
- From the Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)
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Zhou Z, Qian X, Hu J, Geng C, Zhang Y, Dou X, Che T, Zhu J, Dai Y. Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Front Oncol 2023; 13:1167328. [PMID: 37692840 PMCID: PMC10485140 DOI: 10.3389/fonc.2023.1167328] [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: 02/16/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yongsheng Zhang
- Department of Pathology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xin Dou
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tuanjie Che
- Key Laboratory of Functional Genomic and Molecular Diagnosis of Gansu Province, Lanzhou, Gansu, China
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
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Wang H, Xie M, Chen X, Zhu J, Zhang L, Ding H, Pan Z, He L. Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma. Insights Imaging 2023; 14:106. [PMID: 37316589 DOI: 10.1186/s13244-023-01418-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/30/2023] [Indexed: 06/16/2023] Open
Abstract
PURPOSE To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. METHODS We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n = 89). To balance the classes in the training group, a Synthetic Minority Over-sampling Technique was applied. A logistic regression radiomics model based on the radiomics features after dimensionality reduction was then constructed and validated in both the training and testing groups. To evaluate the diagnostic performance of the radiomics model, the receiver operating characteristic curve and calibration curve were utilized. Moreover, the decision curve analysis to assess the net benefits of the radiomics model at different high-risk thresholds was employed. RESULTS Seventeen radiomics features were used to construct radiomics model. In the training group, radiomics model achieved an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.851 (95% confidence interval (CI) 0.805-0.897), 0.770, 0.694, and 0.847, respectively. In the testing group, radiomics model achieved an AUC, accuracy, sensitivity, and specificity of 0.816 (95% CI 0.725-0.906), 0.787, 0.793, and 0.778, respectively. The calibration curve indicated that the radiomics model was well fitted in both the training and testing groups (p > 0.05). Decision curve analysis further confirmed that the radiomics model performed well at different high-risk thresholds. CONCLUSION Radiomics analysis of contrast-enhanced CT demonstrates favorable diagnostic capabilities in distinguishing the INPC subgroups of neuroblastoma. CRITICAL RELEVANCE STATEMENT Radiomics features of contrast-enhanced CT images correlate with the International Neuroblastoma Pathology Classification (INPC) of neuroblastoma.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Mingye Xie
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Jin Zhu
- Department of Pathology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Li Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Zhengxia Pan
- Department of Cardiothoracic Surgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
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Wang H, Xie M, Chen X, Zhu J, Ding H, Zhang L, Pan Z, He L. Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system. Pediatr Blood Cancer 2023; 70:e30280. [PMID: 36881504 DOI: 10.1002/pbc.30280] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas. PROCEDURE This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated. RESULTS The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively. CONCLUSIONS CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Mingye Xie
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Zhu
- Department of Pathology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Zhengxia Pan
- Department of Cardiothoracic Surgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study. Abdom Radiol (NY) 2023; 48:1372-1382. [PMID: 36892608 DOI: 10.1007/s00261-023-03862-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/10/2023]
Abstract
PURPOSE To examine the potential of whole-tumor radiomics analysis of T2-weighted imaging (T2WI) in differentiating neuroblastoma (NB) from ganglioneuroblastoma/ganglioneuroma (GNB/GN) in children. MATERIALS AND METHODS This study included 102 children with peripheral neuroblastic tumors, comprising 47 NB patients and 55 GNB/GN patients, which were randomly divided into a training group (n = 72) and a test group (n = 30). Radiomics features were extracted from T2WI images, and feature dimensionality reduction was applied. Linear discriminant analysis was used to construct radiomics models, and one-standard error role combined with leave-one-out cross-validation was used to choose the optimal radiomics model with the least predictive error. Subsequently, the patient age at initial diagnosis and the selected radiomics features were incorporated to construct a combined model. The receiver operator characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC) were applied to evaluate the diagnostic performance and clinical utility of the models. RESULTS Fifteen radiomics features were eventually chosen to construct the optimal radiomics model. The area under the curve (AUC) of the radiomics model in the training group and test group was 0.940 [95% confidence interval (CI) 0.886, 0.995] and 0.799 (95%CI 0.632, 0.966), respectively. The combined model, which incorporated patient age and radiomics features, achieved an AUC of 0.963 (95%CI 0.925, 1.000) in the training group and 0.871 (95%CI 0.744, 0.997) in the test group. DCA and CIC demonstrated that the radiomics model and combined model could provide benefits at various thresholds, with the combined model being superior to the radiomics model. CONCLUSION Radiomics features derived from T2WI, in combination with the age of the patient at initial diagnosis, may offer a quantitative method for distinguishing NB from GNB/GN, thus aiding in the pathological differentiation of peripheral neuroblastic tumors in children.
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Wang H, Qin J, Chen X, Zhang T, Zhang L, Ding H, Pan Z, He L. Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma. Abdom Radiol (NY) 2023; 48:976-986. [PMID: 36571609 DOI: 10.1007/s00261-022-03774-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/27/2022]
Abstract
PURPOSE To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma. MATERIALS AND METHODS Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal-Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset. RESULTS The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (Z = 2.003, P = 0.045). The decision curve analysis showed that the combined model performs better than the clinical model. CONCLUSION The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Jinjie Qin
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Ting Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Li Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Zhengxia Pan
- Department of Cardiothoracic Surgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
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Association of image-defined risk factors with clinical features, tumor biology, and outcomes in neuroblastoma: a single-center retrospective study. Eur J Pediatr 2023; 182:2189-2196. [PMID: 36856889 DOI: 10.1007/s00431-023-04899-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/02/2023]
Abstract
Image-defined risk factors (IDRF) in neuroblastoma have been developed to predict tumor resectability and surgical complications; however, the potential prognostic value of IDRF in neuroblastoma has been variably reported. Previous studies did not report the IDRF status separately from the International Neuroblastoma Risk Group (INRG) stage. Moreover, the association between IDRF and clinical and pathological factors has not been discussed further. In this retrospective study, we investigated the clinical and biological features of neuroblastoma at different INRG stages based on IDRF. Event-free survival (EFS) and overall survival (OS) related to the INRG stage were analyzed using log-rank tests, and the prognostic value of the IDRF number and type was also evaluated. Among 72 patients, 182 IDRF at diagnosis were found in 79.2%. The distribution of the INRG stages was 10 L1 (13.9.0%), 25 L2 (34.7%), and 37 M/MS (51.4%). Patients with stage M/Ms had a larger tumor volume, a higher percentage of age ≥ 18 months, elevated lactate dehydrogenase (LDH) level, elevated ferritin level, and a higher percentage of COG high-risk compared with stage L1 and L2 patients. EFS and OS were similar for stage L1 and L2 tumors but were significantly poorer for metastatic disease. However, EFS (P = 0.06) and OS (P = 0.07) were similar for IDRF-negative and positive neuroblastomas. Patients with stage M/Ms with IDRF-positive had poorer EFS (P = 0.001) and OS (P < 0.001) compared with patients in stage L2. An IDRF ≥ 4, vascular IDRF, and infiltrative IDRF of the tumor were significant indicators of poor prognosis. Conclusion: Our study indicates that increasing the INRG stages based on IDRF is associated with various unfavorable clinical features of neuroblastoma. The principal determinant of survival in neuroblastoma is the presence of metastatic disease more than IDRF alone at diagnosis. Both the number and type of IDRF have important clinical significance in the protocol planning of neuroblastoma, rather than just considering the absence or presence of IDRF. What is Known: • The International Neuroblastoma Risk Group Staging System (INRGSS) now employs image-defined risk factors (IDRFs) to stratify and stage disease. • The presence of IDRF at diagnosis are associated with higher rates of operative complications and incomplete surgical resection. What is New: • The principal determinant of survival from neuroblastoma is the presence of metastatic disease at diagnosis, more than IDRF alone. • IDRF number and type should also be considered during the diagnosis and treatment planning of neuroblastoma, rather than just considering the absence or presence of IDRF.
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Li C, Wang S, Li C, Yin Y, Feng F, Fu H, Wang H, Chen S. Improved risk stratification by PET-based intratumor heterogeneity in children with high-risk neuroblastoma. Front Oncol 2022; 12:896593. [PMID: 36353561 PMCID: PMC9637983 DOI: 10.3389/fonc.2022.896593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 11/12/2023] Open
Abstract
PURPOSE The substratification of high-risk neuroblastoma is challenging, and new predictive imaging biomarkers are warranted for better patient selection. The aim of the study was to evaluate the prognostic role of PET-based intratumor heterogeneity and its potential ability to improve risk stratification in neuroblastoma. METHODS Pretreatment 18F-FDG PET/CT scans from 112 consecutive children with newly diagnosed neuroblastoma were retrospectively analyzed. The primary tumor was segmented in the PET images. SUVs, volumetric parameters including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and texture features were extracted. After the exclusion of imaging features with poor and moderate reproducibility, the relationships between the imaging indices and clinicopathological factors, as well as event-free survival (EFS), were assessed. RESULTS The median follow-up duration was 33 months. Multivariate analysis showed that PET-based intratumor heterogeneity outperformed clinicopathological features, including age, stage, and MYCN, and remained the most robust independent predictor for EFS [training set, hazard ratio (HR): 6.4, 95% CI: 3.1-13.2, p < 0.001; test set, HR: 5.0, 95% CI: 1.8-13.6, p = 0.002]. Within the clinical high-risk group, patients with a high metabolic heterogeneity showed significantly poorer outcomes (HR: 3.3, 95% CI: 1.6-6.8, p = 0.002 in the training set; HR: 4.4, 95% CI: 1.5-12.9, p = 0.008 in the test set) compared to those with relatively homogeneous tumors. Furthermore, intratumor heterogeneity outran the volumetric indices (MTVs and TLGs) and yielded the best performance of distinguishing high-risk patients with different outcomes with a 3-year EFS of 6% vs. 47% (p = 0.001) in the training set and 9% vs. 51% (p = 0.004) in the test set. CONCLUSION PET-based intratumor heterogeneity was a strong independent prognostic factor in neuroblastoma. In the clinical high-risk group, intratumor heterogeneity further stratified patients with distinct outcomes.
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Affiliation(s)
- Chao Li
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaoyan Wang
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Can Li
- Department of Pathology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yafu Yin
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Feng
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongliang Fu
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Wang
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Suyun Chen
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang H, Chen X, Zhu J, Zhang K, Lu J, Zhang L, Ding H, He L. Changes in image-defined risk factors with neoadjuvant chemotherapy in pediatric abdominal neuroblastoma. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3520-3530. [PMID: 35790567 DOI: 10.1007/s00261-022-03596-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To observe the changes in image-defined risk factors (IDRFs) with neoadjuvant chemotherapy in pediatric abdominal neuroblastoma and to investigate the correlations between IDRF changes and histopathological features. In addition, this study also investigated the correlations between residual IDRFs after neoadjuvant chemotherapy and intraoperative complications. METHODS Forty-three patients with abdominal neuroblastoma who received neoadjuvant chemotherapy in our hospital from January 2015 to September 2021 were enrolled. Intraoperative records, histopathological features, and CT images at initial diagnosis and after neoadjuvant chemotherapy of all patients were retrospectively collected and analyzed. RESULTS A total of 245 IDRFs were found at initial diagnosis, with a median of 6 [5, 7] IDRFs per patient. After neoadjuvant chemotherapy, IDRFs significantly decreased to 156 (p < 0.001), with a median of 4 [3, 5] IDRFs remaining per patient. The majority of IDRFs (6/8, 75.00%) were significantly improved after neoadjuvant chemotherapy (p < 0.05), while tumor invasion of renal pedicles (p > 0.05) and adjacent structures (p > 0.05) was the least responsive IDRF. IDRFs in different types of neuroblastoma decreased significantly after neoadjuvant chemotherapy (p < 0.05), while they were not significant in neuroblastoma with low and intermediate mitosis-karyorrhexis indices (p > 0.05). The number of residual IDRFs correlated positively with the volumes of intraoperative blood loss (r = 0.399, p = 0.008), but not with the presence of intraoperative complications (r = 0.111, p = 0.478). CONCLUSIONS IDRFs in different types of neuroblastoma can be significantly improved after neoadjuvant chemotherapy, while IDRFs in neuroblastoma with low and intermediate mitosis-karyorrhexis indices might not be easily improved. At the same time, the number of residual IDRFs after neoadjuvant chemotherapy might not correlate with the occurrence of intraoperative complications in abdominal neuroblastoma.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Xin Chen
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Jin Zhu
- Department of Pathology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Ke Zhang
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Jiandong Lu
- Department of Urology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Li Zhang
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Hao Ding
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
| | - Ling He
- Department of Radiology, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
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Feng L, Yang X, Lu X, Kan Y, Wang C, Sun D, Zhang H, Wang W, Yang J. 18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma. Insights Imaging 2022; 13:144. [PMID: 36057694 PMCID: PMC9440965 DOI: 10.1186/s13244-022-01283-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/07/2022] [Indexed: 11/10/2022] Open
Abstract
Objective To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma. Methods A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). Results Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility. Conclusion The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01283-8.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Chao Wang
- Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, 100192, China
| | - Dehui Sun
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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Tan E, Merchant K, Kn BP, Cs A, Zhao JJ, Saffari SE, Tan PH, Tang PH. CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma. Childs Nerv Syst 2022; 38:1487-1495. [PMID: 35460355 DOI: 10.1007/s00381-022-05534-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/13/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)-based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma. METHODS From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children's hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features. RESULTS One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85-1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64-1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher "GreyLevelNonUniformity" values, lower "Strength" values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification. CONCLUSION The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.
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Affiliation(s)
- Eelin Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore.
| | - Khurshid Merchant
- Department of Pathology and Laboratory Medicine, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Bhanu Prakash Kn
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Arvind Cs
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Joseph J Zhao
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore
| | - Seyed Ehsan Saffari
- Center for Quantitative Medicine, Duke-NUS Graduate Medical School, 8 College Rd, Singapore, 169857, Singapore
| | - Poh Hwa Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Phua Hwee Tang
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
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Ghosh A, Yekeler E, Dalal D, Holroyd A, States L. Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results. Eur Radiol 2022; 32:8453-8462. [PMID: 35437614 DOI: 10.1007/s00330-022-08750-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/27/2022] [Accepted: 03/11/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas. METHODS We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7-24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma. Histogram parameters of ADC maps from the entire tumour was obtained from the baseline images and the normalised images. The Mann-Whitney U test was used to compare the absolute and normalised histogram parameters amongst neuroblastomas with and without MYCN-amplification. Receiver operating characteristic (ROC) curves and area under the curves (AUC) were generated for the statistically significant histogram parameters. Cut-offs obtained from the ROC curves were evaluated on an external validation set (n-15, MYCNA-6, F-7, age 24 months (10-60)). A logistic regression model was trained to predict MYCNA by combining statistically significant histogram parameters and was evaluated on the validation set. RESULTS MYCN-amplified neuroblastomas had statistically significant higher maximum ADC and lower minimum ADC than non-amplified neuroblastomas. They also demonstrated higher entropy, variance, energy, and lower uniformity than non-amplified neoplasms (p > 0.05). Energy, entropy, and maximum ADC had AUC of 0.85, 0.79, and 0.82, respectively. CONCLUSIONS Whole tumour ADC histogram analysis of neuroblastomas can differentiate between tumours with and without MYCN-amplification. These parameters can help identify areas for targeted biopsies or can be used to predict subtypes of these high-risk tumours before biopsy results are available. KEY POINTS • MYCN-amplification significantly affects treatment decisions in neuroblastomas. • MYCN-amplified neuroblastomas had significantly different ADC histogram metrics as compared to tumours without amplification. • ADC histogram metrics can be used to predict MYCN-amplification status based on imaging.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, Office 3122, 3rd Floor, 2716 South Street, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
| | - Ensar Yekeler
- Department of Radiology, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, Office 3122, 3rd Floor, 2716 South Street, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Deepa Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, Office 3122, 3rd Floor, 2716 South Street, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Alexandria Holroyd
- Department of Radiology, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, Office 3122, 3rd Floor, 2716 South Street, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Lisa States
- Department of Radiology, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, Office 3122, 3rd Floor, 2716 South Street, 3401 Civic Center Boulevard, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics. Diagnostics (Basel) 2022; 12:diagnostics12020262. [PMID: 35204353 PMCID: PMC8871335 DOI: 10.3390/diagnostics12020262] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 12/23/2022] Open
Abstract
Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of 18F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.
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